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Levin C, Naimi E, Saban M. Evaluating GenAI systems to combat mental health issues in healthcare workers: An integrative literature review. Int J Med Inform 2024; 191:105566. [PMID: 39079316 DOI: 10.1016/j.ijmedinf.2024.105566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 06/10/2024] [Accepted: 07/21/2024] [Indexed: 09/07/2024]
Abstract
BACKGROUND Mental health issues among healthcare workers remain a serious problem globally. Recent surveys continue to report high levels of depression, anxiety, burnout and other conditions amongst various occupational groups. Novel approaches are needed to support clinician well-being. OBJECTIVE This integrative literature review aims to explore the current state of research examining the use of generative artificial intelligence (GenAI) and machine learning (ML) systems to predict mental health issues and identify associated risk factors amongst healthcare professionals. METHODS A literature search of databases was conducted in Medline then adapted as necessary to Scopus, Web of Science, Google Scholar, PubMed and CINAHL with Full Text. Eleven studies met the inclusion criteria for the review. RESULTS Nine studies employed various machine learning techniques to predict different mental health outcomes among healthcare workers. Models showed good predictive performance, with AUCs ranging from 0.82 to 0.904 for outcomes such as depression, anxiety and safety perceptions. Key risk factors identified included fatigue, stress, burnout, workload, sleep issues and lack of support. Two studies explored the potential of sensor-based technologies and GenAI analysis of physiological data. None of the included studies focused on the use of GenAI systems specifically for providing mental health support to healthcare workers. CONCLUSION Preliminary research demonstrates that AI/ML models can effectively predict mental health issues. However, more work is needed to evaluate the real-world integration and impact of these tools, including GenAI systems, in identifying clinician distress and supporting well-being over time. Further research should aim to explore how GenAI may be developed and applied to provide mental health support for healthcare workers.
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Affiliation(s)
- C Levin
- Faculty of School of Life and Health Sciences, Nursing Department, The Jerusalem College of Technology-Lev Academic Center, Jerusalem, Israel; The Department of Vascular Surgery, The Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Tel Aviv, Israel
| | - E Naimi
- Department of Nursing, School of Health Professions, Faculty of Medicine, Tel Aviv University
| | - M Saban
- Department of Nursing, School of Health Professions, Faculty of Medicine, Tel Aviv University.
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2
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Oliver D, Arribas M, Perry BI, Whiting D, Blackman G, Krakowski K, Seyedsalehi A, Osimo EF, Griffiths SL, Stahl D, Cipriani A, Fazel S, Fusar-Poli P, McGuire P. Using Electronic Health Records to Facilitate Precision Psychiatry. Biol Psychiatry 2024; 96:532-542. [PMID: 38408535 DOI: 10.1016/j.biopsych.2024.02.1006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/30/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024]
Abstract
The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.
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Affiliation(s)
- Dominic Oliver
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, United Kingdom; Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Maite Arribas
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Daniel Whiting
- Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
| | - Graham Blackman
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Kamil Krakowski
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Aida Seyedsalehi
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Emanuele F Osimo
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom; Imperial College London Institute of Clinical Sciences and UK Research and Innovation MRC London Institute of Medical Sciences, Hammersmith Hospital Campus, London, United Kingdom; South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Siân Lowri Griffiths
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Andrea Cipriani
- NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy; South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; NIHR Oxford Health Biomedical Research Centre, Oxford, United Kingdom; OPEN Early Detection Service, Oxford Health NHS Foundation Trust, Oxford, United Kingdom
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3
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Tavory T. Regulating AI in Mental Health: Ethics of Care Perspective. JMIR Ment Health 2024; 11:e58493. [PMID: 39298759 DOI: 10.2196/58493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 06/29/2024] [Accepted: 07/20/2024] [Indexed: 09/22/2024] Open
Abstract
This article contends that the responsible artificial intelligence (AI) approach-which is the dominant ethics approach ruling most regulatory and ethical guidance-falls short because it overlooks the impact of AI on human relationships. Focusing only on responsible AI principles reinforces a narrow concept of accountability and responsibility of companies developing AI. This article proposes that applying the ethics of care approach to AI regulation can offer a more comprehensive regulatory and ethical framework that addresses AI's impact on human relationships. This dual approach is essential for the effective regulation of AI in the domain of mental health care. The article delves into the emergence of the new "therapeutic" area facilitated by AI-based bots, which operate without a therapist. The article highlights the difficulties involved, mainly the absence of a defined duty of care toward users, and shows how implementing ethics of care can establish clear responsibilities for developers. It also sheds light on the potential for emotional manipulation and the risks involved. In conclusion, the article proposes a series of considerations grounded in the ethics of care for the developmental process of AI-powered therapeutic tools.
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Affiliation(s)
- Tamar Tavory
- Faculty of Law, Bar Ilan University, Ramat Gan, Israel
- The Samueli Initiative for Responsible AI in Medicine, Tel Aviv University, Tel Aviv, Israel
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4
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Cosic K, Kopilas V, Jovanovic T. War, emotions, mental health, and artificial intelligence. Front Psychol 2024; 15:1394045. [PMID: 39156807 PMCID: PMC11327060 DOI: 10.3389/fpsyg.2024.1394045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 07/24/2024] [Indexed: 08/20/2024] Open
Abstract
During the war time dysregulation of negative emotions such as fear, anger, hatred, frustration, sadness, humiliation, and hopelessness can overrule normal societal values, culture, and endanger global peace and security, and mental health in affected societies. Therefore, it is understandable that the range and power of negative emotions may play important roles in consideration of human behavior in any armed conflict. The estimation and assessment of dominant negative emotions during war time are crucial but are challenged by the complexity of emotions' neuro-psycho-physiology. Currently available natural language processing (NLP) tools have comprehensive computational methods to analyze and understand the emotional content of related textual data in war-inflicted societies. Innovative AI-driven technologies incorporating machine learning, neuro-linguistic programming, cloud infrastructure, and novel digital therapeutic tools and applications present an immense potential to enhance mental health care worldwide. This advancement could make mental health services more cost-effective and readily accessible. Due to the inadequate number of psychiatrists and limited psychiatric resources in coping with mental health consequences of war and traumas, new digital therapeutic wearable devices supported by AI tools and means might be promising approach in psychiatry of future. Transformation of negative dominant emotional maps might be undertaken by the simultaneous combination of online cognitive behavioral therapy (CBT) on individual level, as well as usage of emotionally based strategic communications (EBSC) on a public level. The proposed positive emotional transformation by means of CBT and EBSC may provide important leverage in efforts to protect mental health of civil population in war-inflicted societies. AI-based tools that can be applied in design of EBSC stimuli, like Open AI Chat GPT or Google Gemini may have great potential to significantly enhance emotionally based strategic communications by more comprehensive understanding of semantic and linguistic analysis of available text datasets of war-traumatized society. Human in the loop enhanced by Chat GPT and Gemini can aid in design and development of emotionally annotated messages that resonate among targeted population, amplifying the impact of strategic communications in shaping human dominant emotional maps into a more positive by CBT and EBCS.
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Affiliation(s)
- Kresimir Cosic
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Vanja Kopilas
- University of Zagreb Faculty of Croatian Studies, Zagreb, Croatia
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
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5
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Lawrence HR, Schneider RA, Rubin SB, Matarić MJ, McDuff DJ, Jones Bell M. The Opportunities and Risks of Large Language Models in Mental Health. JMIR Ment Health 2024; 11:e59479. [PMID: 39105570 DOI: 10.2196/59479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 05/31/2024] [Accepted: 06/01/2024] [Indexed: 08/07/2024] Open
Abstract
Unlabelled Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally.
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Affiliation(s)
| | | | | | - Maja J Matarić
- Google LLC, Mountain View, CA, 90291, United States, 13103106000
| | - Daniel J McDuff
- Google LLC, Mountain View, CA, 90291, United States, 13103106000
| | - Megan Jones Bell
- Google LLC, Mountain View, CA, 90291, United States, 13103106000
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6
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Glaudemans AW. Heliyon medical imaging: Shaping the future of health. Heliyon 2024; 10:e32395. [PMID: 39183843 PMCID: PMC11341280 DOI: 10.1016/j.heliyon.2024.e32395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 06/03/2024] [Indexed: 08/27/2024] Open
Affiliation(s)
- Andor W.J.M. Glaudemans
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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7
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Newby D, Taylor N, Joyce DW, Winchester LM. Optimising the use of electronic medical records for large scale research in psychiatry. Transl Psychiatry 2024; 14:232. [PMID: 38824136 PMCID: PMC11144247 DOI: 10.1038/s41398-024-02911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 06/03/2024] Open
Abstract
The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.
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Affiliation(s)
- Danielle Newby
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Niall Taylor
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Dan W Joyce
- Department of Primary Care and Mental Health and Civic Health, Innovation Labs, Institute of Population Health, University of Liverpool, Liverpool, UK
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8
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O'Connor C. Public perspectives on AI diagnosis of mental illness. Gen Psychiatr 2024; 37:e101370. [PMID: 38800631 PMCID: PMC11116862 DOI: 10.1136/gpsych-2023-101370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
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9
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Yoo DW, Woo H, Nguyen VC, Birnbaum ML, Kruzan KP, Kim JG, Abowd GD, De Choudhury M. Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment. PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. CHI CONFERENCE 2024; 2024:702. [PMID: 38894725 PMCID: PMC11184595 DOI: 10.1145/3613904.3642369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.
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Affiliation(s)
| | - Hayoung Woo
- Georgia Institute of Technology, Atlanta, Georgia, USA
| | | | | | | | | | - Gregory D Abowd
- Northeastern University, Boston, Massachusetts, USA, Georgia Institute of Technology, Atlanta, Georgia, USA
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10
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He J, Ahmad SF, Al-Razgan M, Ali YA, Irshad M. Factors affecting the adoption of metaverse in healthcare: The moderating role of digital division, and meta-culture. Heliyon 2024; 10:e28778. [PMID: 38633630 PMCID: PMC11021906 DOI: 10.1016/j.heliyon.2024.e28778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 03/24/2024] [Accepted: 03/25/2024] [Indexed: 04/19/2024] Open
Abstract
This research aims to find out the factors affecting the adoption of Metaverse in healthcare. This study explores the effect of perceived ease of use, perceived usefulness, and trust on adopting Metaverse in healthcare by keeping digital division and metaculture as moderating variables. The philosophical foundation is rooted in the positivism paradigm, the methodology is quantitative, and the approach used is deductive. Data was collected in Pakistan and China through judgmental sampling from 384 respondents. Partial Least Square Structural Equation Modelling (PLS-SEM) was used to analyze the collected data. The findings validate the relationship between perceived ease of use and the adoption of metaverse with β-value 0.236, t-value 5.207 and p-value 0.000, the relationship between perceived usefulness and the adoption of metaverse with β-value 0.233, t-value 4.017 and p-value 0.000, and the relationship between trust and adoption of a metaverse with β-value 0.192, t-value 3.589 and p-value 0.000. Results also show that the digital divide moderates the relation between perceived ease of use and adopting the metaverse having β-value 0.078, t-value 1.848 and p-value 0.032. Similarly, the findings also show that the digital divide does not moderate the relationships of perceived usefulness and trust with adopting the metaverse. Moreover, the meta culture also does not moderate the relationships of perceived ease of use, usefulness, and trust with adopting the metaverse. The study contributes to theoretical research on adopting a metaverse in healthcare by examining various factors necessary for its development. It also provides guidelines for the developers and adopters of suitable metaverse technology.
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Affiliation(s)
- Jibo He
- School of Psychology, Nanjing Normal University, Nanjing, 210023, China
| | - Sayed Fayaz Ahmad
- Department of Engineering Management, Institute of Business Management, Karachi, Pakistan
| | - Muna Al-Razgan
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Yasser A. Ali
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Muhammad Irshad
- Department of Management Sciences, University of Gwadar, Pakistan
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11
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Thakkar A, Gupta A, De Sousa A. Artificial intelligence in positive mental health: a narrative review. Front Digit Health 2024; 6:1280235. [PMID: 38562663 PMCID: PMC10982476 DOI: 10.3389/fdgth.2024.1280235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.
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12
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Bala J, Newson JJ, Thiagarajan TC. Hierarchy of demographic and social determinants of mental health: analysis of cross-sectional survey data from the Global Mind Project. BMJ Open 2024; 14:e075095. [PMID: 38490653 PMCID: PMC10946366 DOI: 10.1136/bmjopen-2023-075095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 02/16/2024] [Indexed: 03/17/2024] Open
Abstract
OBJECTIVES To understand the extent to which various demographic and social determinants predict mental health status and their relative hierarchy of predictive power in order to prioritise and develop population-based preventative approaches. DESIGN Cross-sectional analysis of survey data. SETTING Internet-based survey from 32 countries across North America, Europe, Latin America, Middle East and North Africa, Sub-Saharan Africa, South Asia and Australia, collected between April 2020 and December 2021. PARTICIPANTS 270 000 adults aged 18-85+ years who participated in the Global Mind Project. OUTCOME MEASURES We used 120+ demographic and social determinants to predict aggregate mental health status and scores of individuals (mental health quotient (MHQ)) and determine their relative predictive influence using various machine learning models including gradient boosting and random forest classification for various demographic stratifications by age, gender, geographical region and language. Outcomes reported include model performance metrics of accuracy, precision, recall, F1 scores and importance of individual factors determined by reduction in the squared error attributable to that factor. RESULTS Across all demographic classification models, 80% of those with negative MHQs were correctly identified, while regression models predicted specific MHQ scores within ±15% of the position on the scale. Predictions were higher for older ages (0.9+ accuracy, 0.9+ F1 Score; 65+ years) and poorer for younger ages (0.68 accuracy, 0.68 F1 Score; 18-24 years). Across all age groups, genders, regions and language groups, lack of social interaction and sufficient sleep were several times more important than all other factors. For younger ages (18-24 years), other highly predictive factors included cyberbullying and sexual abuse while not being able to work was high for ages 45-54 years. CONCLUSION Social determinants of traumas, adversities and lifestyle can account for 60%-90% of mental health challenges. However, additional factors are at play, particularly for younger ages, that are not included in these data and need further investigation.
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13
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DelPozo-Banos M, Stewart R, John A. Machine learning in mental health and its relationship with epidemiological practice. Front Psychiatry 2024; 15:1347100. [PMID: 38528983 PMCID: PMC10961376 DOI: 10.3389/fpsyt.2024.1347100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/22/2024] [Indexed: 03/27/2024] Open
Affiliation(s)
| | - Robert Stewart
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
- South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Ann John
- Swansea University Medical School, Swansea, United Kingdom
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14
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Habicht J, Viswanathan S, Carrington B, Hauser TU, Harper R, Rollwage M. Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot. Nat Med 2024; 30:595-602. [PMID: 38317020 DOI: 10.1038/s41591-023-02766-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 12/14/2023] [Indexed: 02/07/2024]
Abstract
Inequality in treatment access is a pressing issue in most healthcare systems across many medical disciplines. In mental healthcare, reduced treatment access for minorities is ubiquitous but remedies are sparse. Here we demonstrate that digital tools can reduce the accessibility gap by addressing several key barriers. In a multisite observational study of 129,400 patients within England's NHS services, we evaluated the impact of a personalized artificial intelligence-enabled self-referral chatbot on patient referral volume and diversity in ethnicity, gender and sexual orientation. We found that services that used this digital solution identified substantially increased referrals (15% increase versus 6% increase in control services). Critically, this increase was particularly pronounced in minorities, such as nonbinary (179% increase) and ethnic minority individuals (29% increase). Using natural language processing to analyze qualitative feedback from 42,332 individuals, we found that the chatbot's human-free nature and the patients' self-realization of their need for treatment were potential drivers for the observed improvement in the diversity of access. This provides strong evidence that digital tools may help overcome the pervasive inequality in mental healthcare.
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Affiliation(s)
| | | | | | - Tobias U Hauser
- Limbic, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Department of Psychiatry and Psychotherapy, Medical School and University Hospital, Eberhard Karls University of Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Tübingen, Germany
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15
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Rogan J, Bucci S, Firth J. Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis. JMIR Ment Health 2024; 11:e49577. [PMID: 38261403 PMCID: PMC10848143 DOI: 10.2196/49577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation. OBJECTIVE This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care. METHODS A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework. RESULTS Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues. CONCLUSIONS Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.
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Affiliation(s)
- Jessica Rogan
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Sandra Bucci
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
- Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Joseph Firth
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Sciences, The University of Manchester, Manchester, United Kingdom
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16
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Wagner E, Luykx JJ, Strube W, Hasan A. Challenges, unmet needs and future directions - a critical evaluation of the clinical trial landscape in schizophrenia research. Expert Rev Clin Pharmacol 2024; 17:11-18. [PMID: 38087450 DOI: 10.1080/17512433.2023.2293996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023]
Abstract
INTRODUCTION Developing novel antipsychotic mechanisms of action and repurposing established compounds for the treatment of schizophrenia is of utmost importance to improve relevant symptom domains and to improve the risk/benefit ratio of antipsychotic compounds. Novel trial design concepts, pathophysiology-based targeted treatment approaches, or even the return to old values may improve schizophrenia outcomes in the future. AREAS COVERED In this review of the clinical trial landscape in schizophrenia, we present an overview of the challenges and gaps in current clinical trials and elaborate on potential solutions to improve the outcomes of people with schizophrenia. EXPERT OPINION The classic parallel group design may limit substantial advantages in drug approval or repurposing. Collaborative approaches between regulatory authorities, industry, academia, and funding agencies are needed to overcome barriers in clinical schizophrenia research to allow for meaningful outcome improvements for the patients.
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Affiliation(s)
- Elias Wagner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, University of Augsburg, Augsburg, Germany
- Evidence-based psychiatry and psychotherapy, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Jurjen J Luykx
- Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, the Netherlands
- Bipolar Outpatient Clinic, GGZ inGeest Mental Health Care, Amsterdam, The Netherlands
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Wolfgang Strube
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Alkomiet Hasan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, University of Augsburg, Augsburg, Germany
- DZPG (German Center for Mental Health), partner site München/Augsburg, Augsburg, Germany
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17
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Irmak-Yazicioglu MB, Arslan A. Navigating the Intersection of Technology and Depression Precision Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1456:401-426. [PMID: 39261440 DOI: 10.1007/978-981-97-4402-2_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
This chapter primarily focuses on the progress in depression precision medicine with specific emphasis on the integrative approaches that include artificial intelligence and other data, tools, and technologies. After the description of the concept of precision medicine and a comparative introduction to depression precision medicine with cancer and epilepsy, new avenues of depression precision medicine derived from integrated artificial intelligence and other sources will be presented. Additionally, less advanced areas, such as comorbidity between depression and cancer, will be examined.
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Affiliation(s)
| | - Ayla Arslan
- Department of Molecular Biology and Genetics, Üsküdar University, İstanbul, Türkiye.
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18
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Zhong G, Su H, Zhao D, Hu J, Liu X, Li Y, Semnanian S, Haghparast A, Yuan TF, Du J. Cooperation between China and Iran in addiction medicine: opportunities, challenges and strategies. Gen Psychiatr 2023; 36:e101162. [PMID: 38155844 PMCID: PMC10753709 DOI: 10.1136/gpsych-2023-101162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 12/03/2023] [Indexed: 12/30/2023] Open
Affiliation(s)
- Gangliang Zhong
- Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hang Su
- Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Zhao
- Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ji Hu
- Shanghai Tech University, Shanghai, China
| | - Xing Liu
- Department of Neurosurgery, Fudan University, Shanghai, China
| | - Yonghui Li
- Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of the Chinese Academy of Sciences, Beijing, China
| | - Saeed Semnanian
- Department of Physiology, Tarbiat Modares University, Tehran, Iran
| | - Abbas Haghparast
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Department of Basic Sciences, Academy of Medical Sciences, Tehran, Iran
| | - Ti-Fei Yuan
- Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiang Du
- Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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19
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Li H, Zhang R, Lee YC, Kraut RE, Mohr DC. Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being. NPJ Digit Med 2023; 6:236. [PMID: 38114588 PMCID: PMC10730549 DOI: 10.1038/s41746-023-00979-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 11/29/2023] [Indexed: 12/21/2023] Open
Abstract
Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs' effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge's g 0.64 [95% CI 0.17-1.12]) and distress (Hedge's g 0.7 [95% CI 0.18-1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge's g 0.32 [95% CI -0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.
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Affiliation(s)
- Han Li
- Department of Communications and New Media, National University of Singapore, Singapore, 117416, Singapore
| | - Renwen Zhang
- Department of Communications and New Media, National University of Singapore, Singapore, 117416, Singapore.
| | - Yi-Chieh Lee
- Department of Computer Science, National University of Singapore, Singapore, 117416, Singapore
| | - Robert E Kraut
- Human-Computer Interaction Institute Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - David C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA
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20
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Rollwage M, Habicht J, Juechems K, Carrington B, Viswanathan S, Stylianou M, Hauser TU, Harper R. Using Conversational AI to Facilitate Mental Health Assessments and Improve Clinical Efficiency Within Psychotherapy Services: Real-World Observational Study. JMIR AI 2023; 2:e44358. [PMID: 38875569 PMCID: PMC11041479 DOI: 10.2196/44358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/31/2023] [Accepted: 10/20/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Most mental health care providers face the challenge of increased demand for psychotherapy in the absence of increased funding or staffing. To overcome this supply-demand imbalance, care providers must increase the efficiency of service delivery. OBJECTIVE In this study, we examined whether artificial intelligence (AI)-enabled digital solutions can help mental health care practitioners to use their time more efficiently, and thus reduce strain on services and improve patient outcomes. METHODS In this study, we focused on the use of an AI solution (Limbic Access) to support initial patient referral and clinical assessment within the UK's National Health Service. Data were collected from 9 Talking Therapies services across England, comprising 64,862 patients. RESULTS We showed that the use of this AI solution improves clinical efficiency by reducing the time clinicians spend on mental health assessments. Furthermore, we found improved outcomes for patients using the AI solution in several key metrics, such as reduced wait times, reduced dropout rates, improved allocation to appropriate treatment pathways, and, most importantly, improved recovery rates. When investigating the mechanism by which the AI solution achieved these improvements, we found that the provision of clinically relevant information ahead of clinical assessment was critical for these observed effects. CONCLUSIONS Our results emphasize the utility of using AI solutions to support the mental health workforce, further highlighting the potential of AI solutions to increase the efficiency of care delivery and improve clinical outcomes for patients.
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Affiliation(s)
| | | | | | | | | | | | - Tobias U Hauser
- Limbic Limited, London, United Kingdom
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
- Department of Psychiatry and Psychotherapy, Medical School and University Hospital, Eberhard Karls University of Tübingen, Tubingen, Germany
- German Center for Mental Health (DZPG), Tubingen, Germany
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21
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Smith A, Hachen S, Schleifer R, Bhugra D, Buadze A, Liebrenz M. Old dog, new tricks? Exploring the potential functionalities of ChatGPT in supporting educational methods in social psychiatry. Int J Soc Psychiatry 2023; 69:1882-1889. [PMID: 37392000 DOI: 10.1177/00207640231178451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/02/2023]
Abstract
BACKGROUND Artificial Intelligence is ever-expanding and large-language models are increasingly shaping teaching and learning experiences. ChatGPT is a prominent recent example of this technology and has generated much debate around the benefits and disadvantages of chatbots in educational domains. AIM This study seeks to demonstrate the possible use-cases of ChatGPT in supporting educational methods specific to social psychiatry. METHODS Through interactions with ChatGPT 3.5, we asked this technology to list six ways in which it could aid social psychiatry teaching. Subsequently, we requested that ChatGPT perform one of the tasks it identified in its responses. FINDINGS ChatGPT highlighted several roles it could fulfil in educational settings, including as an information provider, a tool for debates and discussions, a facilitator of self-directed learning and a content-creator for course materials. For the latter scenario, based on another prompt, ChatGPT generated a hypothetical case vignette for a topic relevant to social psychiatry. CONCLUSIONS Based on our experiences, ChatGPT can be an effective teaching tool, offering opportunities for active and case-based learning for students and instructors in social psychiatry. However, in their current form, chatbots have several limitations that must be considered, including misinformation and inherent biases, although these may only be temporary in nature as these technologies continue to advance. Accordingly, we argue that large-language models can support social psychiatry education with appropriate caution and encourage educators to become attuned to their potential through further detailed research in this area.
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Affiliation(s)
- Alexander Smith
- Department of Forensic Psychiatry, University of Bern, Switzerland
| | - Stefanie Hachen
- Department of Forensic Psychiatry, University of Bern, Switzerland
| | - Roman Schleifer
- Department of Forensic Psychiatry, University of Bern, Switzerland
| | | | - Anna Buadze
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Switzerland
| | - Michael Liebrenz
- Department of Forensic Psychiatry, University of Bern, Switzerland
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22
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Sarkar S, Gaur M, Chen LK, Garg M, Srivastava B. A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement. Front Artif Intell 2023; 6:1229805. [PMID: 37899961 PMCID: PMC10601652 DOI: 10.3389/frai.2023.1229805] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 08/29/2023] [Indexed: 10/31/2023] Open
Abstract
Virtual Mental Health Assistants (VMHAs) continuously evolve to support the overloaded global healthcare system, which receives approximately 60 million primary care visits and 6 million emergency room visits annually. These systems, developed by clinical psychologists, psychiatrists, and AI researchers, are designed to aid in Cognitive Behavioral Therapy (CBT). The main focus of VMHAs is to provide relevant information to mental health professionals (MHPs) and engage in meaningful conversations to support individuals with mental health conditions. However, certain gaps prevent VMHAs from fully delivering on their promise during active communications. One of the gaps is their inability to explain their decisions to patients and MHPs, making conversations less trustworthy. Additionally, VMHAs can be vulnerable in providing unsafe responses to patient queries, further undermining their reliability. In this review, we assess the current state of VMHAs on the grounds of user-level explainability and safety, a set of desired properties for the broader adoption of VMHAs. This includes the examination of ChatGPT, a conversation agent developed on AI-driven models: GPT3.5 and GPT-4, that has been proposed for use in providing mental health services. By harnessing the collaborative and impactful contributions of AI, natural language processing, and the mental health professionals (MHPs) community, the review identifies opportunities for technological progress in VMHAs to ensure their capabilities include explainable and safe behaviors. It also emphasizes the importance of measures to guarantee that these advancements align with the promise of fostering trustworthy conversations.
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Affiliation(s)
- Surjodeep Sarkar
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States
| | - Manas Gaur
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States
| | - Lujie Karen Chen
- Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, United States
| | - Muskan Garg
- Department of AI & Informatics, Mayo Clinic, Rochester, MN, United States
| | - Biplav Srivastava
- AI Institute, University of South Carolina, Columbia, SC, United States
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23
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Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. J Med Internet Res 2023; 25:e44502. [PMID: 37792430 PMCID: PMC10585447 DOI: 10.2196/44502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 07/10/2023] [Accepted: 08/21/2023] [Indexed: 10/05/2023] Open
Abstract
The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.
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Affiliation(s)
- Antoine Oudin
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Redwan Maatoug
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Alexis Bourla
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
- Medical Strategy and Innovation Department, Clariane, Paris, France
- NeuroStim Psychiatry Practice, Paris, France
| | - Florian Ferreri
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Olivier Bonnot
- Department of Child and Adolescent Psychiatry, Nantes University Hospital, Nantes, France
- Pays de la Loire Psychology Laboratory, Nantes University, Nantes, France
| | - Bruno Millet
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Félix Schoeller
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Stéphane Mouchabac
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Vladimir Adrien
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
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24
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Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87:103705. [PMID: 37506575 DOI: 10.1016/j.ajp.2023.103705] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
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Affiliation(s)
- Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Beijing 100191, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Qun-Xi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - San-Wang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yong-Bo Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Xiao-Xing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Tang-Sheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Kai Yuan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Ying Han
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China.
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Nghiem J, Adler DA, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Form Res 2023; 7:e47380. [PMID: 37561561 PMCID: PMC10450536 DOI: 10.2196/47380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 06/20/2023] [Accepted: 07/06/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Digital health-tracking tools are changing mental health care by giving patients the ability to collect passively measured patient-generated health data (PGHD; ie, data collected from connected devices with little to no patient effort). Although there are existing clinical guidelines for how mental health clinicians should use more traditional, active forms of PGHD for clinical decision-making, there is less clarity on how passive PGHD can be used. OBJECTIVE We conducted a qualitative study to understand mental health clinicians' perceptions and concerns regarding the use of technology-enabled, passively collected PGHD for clinical decision-making. Our interviews sought to understand participants' current experiences with and visions for using passive PGHD. METHODS Mental health clinicians providing outpatient services were recruited to participate in semistructured interviews. Interview recordings were deidentified, transcribed, and qualitatively coded to identify overarching themes. RESULTS Overall, 12 mental health clinicians (n=11, 92% psychiatrists and n=1, 8% clinical psychologist) were interviewed. We identified 4 overarching themes. First, passive PGHD are patient driven-we found that current passive PGHD use was patient driven, not clinician driven; participating clinicians only considered passive PGHD for clinical decision-making when patients brought passive data to clinical encounters. The second theme was active versus passive data as subjective versus objective data-participants viewed the contrast between active and passive PGHD as a contrast between interpretive data on patients' mental health and objective information on behavior. Participants believed that prioritizing passive over self-reported, active PGHD would reduce opportunities for patients to reflect upon their mental health, reducing treatment engagement and raising questions about how passive data can best complement active data for clinical decision-making. Third, passive PGHD must be delivered at appropriate times for action-participants were concerned with the real-time nature of passive PGHD; they believed that it would be infeasible to use passive PGHD for real-time patient monitoring outside clinical encounters and more feasible to use passive PGHD during clinical encounters when clinicians can make treatment decisions. The fourth theme was protecting patient privacy-participating clinicians wanted to protect patient privacy within passive PGHD-sharing programs and discussed opportunities to refine data sharing consent to improve transparency surrounding passive PGHD collection and use. CONCLUSIONS Although passive PGHD has the potential to enable more contextualized measurement, this study highlights the need for building and disseminating an evidence base describing how and when passive measures should be used for clinical decision-making. This evidence base should clarify how to use passive data alongside more traditional forms of active PGHD, when clinicians should view passive PGHD to make treatment decisions, and how to protect patient privacy within passive data-sharing programs. Clear evidence would more effectively support the uptake and effective use of these novel tools for both patients and their clinicians.
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Affiliation(s)
- Jodie Nghiem
- Medical College, Weill Cornell Medicine, New York, NY, United States
| | - Daniel A Adler
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Deborah Estrin
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Cecilia Livesey
- Optum Labs, UnitedHealth Group, Minnetonka, MN, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Tanzeem Choudhury
- College of Computing and Information Science, Cornell Tech, New York, NY, United States
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Knights J, Bangieva V, Passoni M, Donegan ML, Shen J, Klein A, Baker J, DuBois H. A framework for precision "dosing" of mental healthcare services: algorithm development and clinical pilot. Int J Ment Health Syst 2023; 17:21. [PMID: 37408006 DOI: 10.1186/s13033-023-00581-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/18/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND One in five adults in the US experience mental illness and over half of these adults do not receive treatment. In addition to the access gap, few innovations have been reported for ensuring the right level of mental healthcare service is available at the right time for individual patients. METHODS Historical observational clinical data was leveraged from a virtual healthcare system. We conceptualize mental healthcare services themselves as therapeutic interventions and develop a prototype computational framework to estimate their potential longitudinal impacts on depressive symptom severity, which is then used to assess new treatment schedules and delivered to clinicians via a dashboard. We operationally define this process as "session dosing": 497 patients who started treatment with severe symptoms of depression between November 2020 and October 2021 were used for modeling. Subsequently, 22 mental health providers participated in a 5-week clinical quality improvement (QI) pilot, where they utilized the prototype dashboard in treatment planning with 126 patients. RESULTS The developed framework was able to resolve patient symptom fluctuations from their treatment schedules: 77% of the modeling dataset fit criteria for using the individual fits for subsequent clinical planning where five anecdotal profile types were identified that presented different clinical opportunities. Based on initial quality thresholds for model fits, 88% of those individuals were identified as adequate for session optimization planning using the developed dashboard, while 12% supported more thorough treatment planning (e.g. different treatment modalities). In the clinical pilot, 90% of clinicians reported using the dashboard a few times or more per member. Although most clinicians (67.5%) either rarely or never used the dashboard to change session types, numerous other discussions were enabled, and opportunities for automating session recommendations were identified. CONCLUSIONS It is possible to model and identify the extent to which mental healthcare services can resolve depressive symptom severity fluctuations. Implementation of one such prototype framework in a real-world clinic represents an advancement in mental healthcare treatment planning; however, investigations to assess which clinical endpoints are impacted by this technology, and the best way to incorporate such frameworks into clinical workflows, are needed and are actively being pursued.
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Affiliation(s)
- Jonathan Knights
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA.
| | - Victoria Bangieva
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Michela Passoni
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Macayla L Donegan
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Jacob Shen
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Audrey Klein
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Justin Baker
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
| | - Holly DuBois
- Mindstrong, Inc., 101 Jefferson Drive, Suite 228, Menlo Park, CA, 94025, USA
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Cascalheira CJ, Flinn RE, Zhao Y, Klooster D, Laprade D, Hamdi SM, Scheer JR, Gonzalez A, Lund EM, Gomez IN, Saha K, De Choudhury M. Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study. JMIR Form Res 2023; 7:e47256. [PMID: 37327053 DOI: 10.2196/47256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/28/2023] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND The optimal treatment for gender dysphoria is medical intervention, but many transgender and nonbinary people face significant treatment barriers when seeking help for gender dysphoria. When untreated, gender dysphoria is associated with depression, anxiety, suicidality, and substance misuse. Technology-delivered interventions for transgender and nonbinary people can be used discretely, safely, and flexibly, thereby reducing treatment barriers and increasing access to psychological interventions to manage distress that accompanies gender dysphoria. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model clinical constructs. OBJECTIVE This study aimed to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP, using transgender and nonbinary people's social media data. METHODS Overall, 6 ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1573 Reddit (Reddit Inc) posts created on transgender- and nonbinary-specific web-based forums. After developing a codebook grounded in clinical science, a research team of clinicians and students experienced in working with transgender and nonbinary clients used qualitative content analysis to determine whether gender dysphoria was present in each Reddit post (ie, the dependent variable). NLP (eg, n-grams, Linguistic Inquiry and Word Count, word embedding, sentiment, and transfer learning) was used to transform the linguistic content of each post into predictors for ML algorithms. A k-fold cross-validation was performed. Hyperparameters were tuned with random search. Feature selection was performed to demonstrate the relative importance of each NLP-generated independent variable in predicting gender dysphoria. Misclassified posts were analyzed to improve future modeling of gender dysphoria. RESULTS Results indicated that a supervised ML algorithm (ie, optimized extreme gradient boosting [XGBoost]) modeled gender dysphoria with a high degree of accuracy (0.84), precision (0.83), and speed (1.23 seconds). Of the NLP-generated independent variables, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) clinical keywords (eg, dysphoria and disorder) were most predictive of gender dysphoria. Misclassifications of gender dysphoria were common in posts that expressed uncertainty, featured a stressful experience unrelated to gender dysphoria, were incorrectly coded, expressed insufficient linguistic markers of gender dysphoria, described past experiences of gender dysphoria, showed evidence of identity exploration, expressed aspects of human sexuality unrelated to gender dysphoria, described socially based gender dysphoria, expressed strong affective or cognitive reactions unrelated to gender dysphoria, or discussed body image. CONCLUSIONS Findings suggest that ML- and NLP-based models of gender dysphoria have significant potential to be integrated into technology-delivered interventions. The results contribute to the growing evidence on the importance of incorporating ML and NLP designs in clinical science, especially when studying marginalized populations.
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Affiliation(s)
- Cory J Cascalheira
- Department of Counseling & Educational Psychology, New Mexico State University, Las Cruces, NM, United States
- Department of Psychology, Syracuse University, Syracuse, NY, United States
| | - Ryan E Flinn
- Augusta University, Augusta, GA, United States
- University of North Dakota, Grand Forks, ND, United States
| | - Yuxuan Zhao
- Department of Counseling & Educational Psychology, New Mexico State University, Las Cruces, NM, United States
| | | | - Danica Laprade
- Northern Arizona University, Flagstaff, AZ, United States
| | - Shah Muhammad Hamdi
- Department of Computer Science, Utah State University, Logan, UT, United States
| | - Jillian R Scheer
- Department of Psychology, Syracuse University, Syracuse, NY, United States
| | | | - Emily M Lund
- University of Alabama, Tuscaloosa, AL, United States
- Ewha Women's University, Seoul, Republic of Korea
| | - Ivan N Gomez
- Department of Counseling & Educational Psychology, New Mexico State University, Las Cruces, NM, United States
| | - Koustuv Saha
- University of Illinois at Urbana-Champaign, Champaign, IL, United States
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Knaust T, Siebler MBD, Tarnogorski D, Skiberowski P, Höllmer H, Moritz C, Schulz H. Cross-sectional field study comparing hippocampal subfields in patients with post-traumatic stress disorder, major depressive disorder, post-traumatic stress disorder with comorbid major depressive disorder, and adjustment disorder using routine clinical data. Front Psychol 2023; 14:1123079. [PMID: 37384185 PMCID: PMC10299169 DOI: 10.3389/fpsyg.2023.1123079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 04/28/2023] [Indexed: 06/30/2023] Open
Abstract
Background The hippocampus is a central brain structure involved in stress processing. Previous studies have linked stress-related mental disorders, such as post-traumatic stress disorder (PTSD) and major depressive disorder (MDD), with changes in hippocampus volume. As PTSD and MDD have similar symptoms, clinical diagnosis relies solely on patients reporting their cognitive and emotional experiences, leading to an interest in utilizing imaging-based data to improve accuracy. Our field study aimed to determine whether there are hippocampal subfield volume differences between stress-related mental disorders (PTSD, MDD, adjustment disorders, and AdjD) using routine clinical data from a military hospital. Methods Participants comprised soldiers (N = 185) with PTSD (n = 50), MDD (n = 70), PTSD with comorbid MDD (n = 38), and AdjD (n = 27). The hippocampus was segmented and volumetrized into subfields automatically using FreeSurfer. We used ANCOVA models with estimated total intracranial volume as a covariate to determine whether there were volume differences in the hippocampal subfields cornu ammonis 1 (CA1), cornu ammonis 2/3 (CA2/3), and dentate gyrus (DG) among patients with PTSD, MDD, PTSD with comorbid MDD, and AdjD. Furthermore, we added self-reported symptom duration and previous psychopharmacological and psychotherapy treatment as further covariates to examine whether there were associations with CA1, CA2/3, and DG. Results No significant volume differences in hippocampal subfields between stress-related mental disorders were found. No significant associations were detected between symptom duration, psychopharmacological treatment, psychotherapy, and the hippocampal subfields. Conclusion Hippocampal subfields may distinguish stress-related mental disorders; however, we did not observe any subfield differences. We provide several explanations for the non-results and thereby inform future field studies.
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Affiliation(s)
- Thiemo Knaust
- Center for Mental Health, Bundeswehr Hospital Hamburg, Hamburg, Germany
| | | | | | | | - Helge Höllmer
- Center for Mental Health, Bundeswehr Hospital Hamburg, Hamburg, Germany
| | - Christian Moritz
- Department of Radiology, Bundeswehr Hospital Hamburg, Hamburg, Germany
| | - Holger Schulz
- Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Encarnação S, Vaz P, Fortunato Á, Forte P, Vaz C, Monteiro AM. Aerobic Fitness as an Important Moderator Risk Factor for Loneliness in Physically Trained Older People: An Explanatory Case Study Using Machine Learning. Life (Basel) 2023; 13:1374. [PMID: 37374156 DOI: 10.3390/life13061374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 05/30/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Loneliness in older people seems to have emerged as an increasingly prevalent social problem. OBJECTIVE To apply a machine learning (ML) algorithm to the task of understanding the influence of sociodemographic variables, physical fitness, physical activity levels (PAL), and sedentary behavior (SB) on the loneliness feelings of physically trained older people. MATERIALS AND METHODS The UCLA loneliness scale was used to evaluate loneliness, the Functional Fitness Test Battery was used to evaluate the correlation of sociodemographic variables, physical fitness, PAL, and SB in the loneliness feelings scores of 23 trained older people (19 women and 4 men). For this purpose, a naive Bayes ML algorithm was applied. RESULTS After analysis, we inferred that aerobic fitness (AF), hand grip strength (HG), and upper limb strength (ULS) comprised the most relevant variables panel to cause high participant loneliness with 100% accuracy and F-1 score. CONCLUSIONS The naive Bayes algorithm with leave-one-out cross-validation (LOOCV) predicted loneliness in trained older with a high precision. In addition, AF was the most potent variable in reducing loneliness risk.
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Affiliation(s)
- Samuel Encarnação
- Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal
- Research Centre in Basic Education (CIEB), Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal
- Department of Pysical Activity and Sport Sciences, Universidad Autónoma de Madrid (UAM), Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
| | - Paula Vaz
- Research Centre in Basic Education (CIEB), Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal
| | - Álvaro Fortunato
- Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal
- Research Centre in Sports Sciences, Health Sciences and Human Development (CIDESD), 5001-801 Vila Real, Portugal
| | - Pedro Forte
- Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal
- Research Centre in Sports Sciences, Health Sciences and Human Development (CIDESD), 5001-801 Vila Real, Portugal
- CI-ISCE, Higher Institute of Educational Sciences of the Douro (ISCE Douro), 4560-708 Penafiel, Portugal
| | - Cátia Vaz
- CI-ISCE, Higher Institute of Educational Sciences of the Douro (ISCE Douro), 4560-708 Penafiel, Portugal
- Department of Education and Supervision, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal
| | - António Miguel Monteiro
- Department of Sport Sciences, Instituto Politécnico de Bragança (IPB), 5300-253 Bragança, Portugal
- Department of Pysical Activity and Sport Sciences, Universidad Autónoma de Madrid (UAM), Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain
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Lagera PGD, Chan SR, Yellowlees PM. Asynchronous Technologies in Mental Health Care and Education. CURRENT TREATMENT OPTIONS IN PSYCHIATRY 2023; 10:1-13. [PMID: 37360962 PMCID: PMC10157570 DOI: 10.1007/s40501-023-00286-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
Purpose of review Patients, providers, and trainees should understand the current types of asynchronous technologies that can be used to enhance the delivery and accessibility of mental health care. Asynchronous telepsychiatry (ATP) removes the need for real time communication between the clinician and patient, which improves efficiency and enables quality specialty care. ATP can be applied as distinct consultative and supervisory models in clinician-to-clinician, clinician-to-patient, and patient-to-mobile health settings. Recent findings This review is based on research literature and the authors' clinical and medical training, using experiences with asynchronous telepsychiatry from before, during, and after the COVID-19 pandemic. Our studies demonstrate that ATP provides positive outcomes in the clinician-to-patient model with demonstrated feasibility, outcomes and patient satisfaction. One author's medical education experience in the Philippines during COVID-19 highlights the potential to utilize asynchronous technology in areas with limitations to online learning. We emphasize the need to teach media skills literacy around mental health to students, coaches, therapists, and clinicians when advocating for mental well-being. Several studies have demonstrated the feasibility of incorporating asynchronous e-tools such as self-guided multimedia and artificial intelligence for data collection at the clinician-to-clinician and patient-to-mobile health level. In addition, we offer fresh perspectives on recent trends in asynchronous telehealth in wellness, applying concepts such as "tele-exercise" and "tele-yoga." Summary Asynchronous technologies continue to be integrated into mental health care services and research. Future research must ensure that the design and the usability of this technology puts the patient and provider first.
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Affiliation(s)
- Pamela Gail D. Lagera
- Division of Hospital Medicine, Clinical Informatics, University of California, San Francisco, CA USA
| | - Steven R. Chan
- Division of Hospital Medicine, Clinical Informatics, University of California, San Francisco, CA USA
- Department of Psychiatry, University of California, San Francisco, CA USA
- Department of Psychiatry, University of California, Davis, CA USA
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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Subhan MA, Parveen F, Shah H, Yalamarty SSK, Ataide JA, Torchilin VP. Recent Advances with Precision Medicine Treatment for Breast Cancer including Triple-Negative Sub-Type. Cancers (Basel) 2023; 15:2204. [PMID: 37190133 PMCID: PMC10137302 DOI: 10.3390/cancers15082204] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Breast cancer is a heterogeneous disease with different molecular subtypes. Breast cancer is the second leading cause of mortality in woman due to rapid metastasis and disease recurrence. Precision medicine remains an essential source to lower the off-target toxicities of chemotherapeutic agents and maximize the patient benefits. This is a crucial approach for a more effective treatment and prevention of disease. Precision-medicine methods are based on the selection of suitable biomarkers to envision the effectiveness of targeted therapy in a specific group of patients. Several druggable mutations have been identified in breast cancer patients. Current improvements in omics technologies have focused on more precise strategies for precision therapy. The development of next-generation sequencing technologies has raised hopes for precision-medicine treatment strategies in breast cancer (BC) and triple-negative breast cancer (TNBC). Targeted therapies utilizing immune checkpoint inhibitors (ICIs), epidermal growth factor receptor inhibitor (EGFRi), poly(ADP-ribose) polymerase inhibitor (PARPi), antibody-drug conjugates (ADCs), oncolytic viruses (OVs), glucose transporter-1 inhibitor (GLUT1i), and targeting signaling pathways are potential treatment approaches for BC and TNBC. This review emphasizes the recent progress made with the precision-medicine therapy of metastatic breast cancer and TNBC.
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Affiliation(s)
- Md Abdus Subhan
- Department of Chemistry, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Farzana Parveen
- Department of Pharmaceutics, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
- Department of Pharmacy Services, DHQ Hospital Jhang 35200, Primary and Secondary Healthcare Department, Government of Punjab, Lahore 54000, Pakistan
| | - Hassan Shah
- Department of Pharmaceutics, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
- CPBN, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA 02115, USA
| | | | - Janaína Artem Ataide
- CPBN, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA 02115, USA
- Faculty of Pharmaceutical Sciences, University of Campinas, Campinas 13083-871, SP, Brazil
| | - Valdimir P. Torchilin
- CPBN, Department of Pharmaceutical Sciences, Northeastern University, Boston, MA 02115, USA
- Department of Chemical Engineering, Northeastern University, Boston, MA 02115, USA
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Abstract
ABSTRACT This article briefly examines the life and work of the late clinical psychologist and philosopher of science Paul E. Meehl. His thesis in Clinical versus Statistical Prediction (1954) that the data combination performed by mechanical operations, as compared to clinicians, achieves higher accuracy in predicting human behavior is one of the earliest theoretical works that laid the groundwork for utilizing statistics and computational modeling in research in psychiatry and clinical psychology. For today's psychiatric researchers and clinicians grappling with the challenges of translating the ever-increasing data of the human mind into practice tools, Meehl's advocacy for both accurate modeling of the data and their clinically relevant use is timely.
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Smith KA, Blease C, Faurholt-Jepsen M, Firth J, Van Daele T, Moreno C, Carlbring P, Ebner-Priemer UW, Koutsouleris N, Riper H, Mouchabac S, Torous J, Cipriani A. Digital mental health: challenges and next steps. BMJ MENTAL HEALTH 2023; 26:e300670. [PMID: 37197797 PMCID: PMC10231442 DOI: 10.1136/bmjment-2023-300670] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/28/2023] [Indexed: 05/19/2023]
Abstract
Digital innovations in mental health offer great potential, but present unique challenges. Using a consensus development panel approach, an expert, international, cross-disciplinary panel met to provide a framework to conceptualise digital mental health innovations, research into mechanisms and effectiveness and approaches for clinical implementation. Key questions and outputs from the group were agreed by consensus, and are presented and discussed in the text and supported by case examples in an accompanying appendix. A number of key themes emerged. (1) Digital approaches may work best across traditional diagnostic systems: we do not have effective ontologies of mental illness and transdiagnostic/symptom-based approaches may be more fruitful. (2) Approaches in clinical implementation of digital tools/interventions need to be creative and require organisational change: not only do clinicians and patients need training and education to be more confident and skilled in using digital technologies to support shared care decision-making, but traditional roles need to be extended, with clinicians working alongside digital navigators and non-clinicians who are delivering protocolised treatments. (3) Designing appropriate studies to measure the effectiveness of implementation is also key: including digital data raises unique ethical issues, and measurement of potential harms is only just beginning. (4) Accessibility and codesign are needed to ensure innovations are long lasting. (5) Standardised guidelines for reporting would ensure effective synthesis of the evidence to inform clinical implementation. COVID-19 and the transition to virtual consultations have shown us the potential for digital innovations to improve access and quality of care in mental health: now is the ideal time to act.
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Affiliation(s)
- Katharine A Smith
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
| | - Charlotte Blease
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Joseph Firth
- Division of Psychology and Mental Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Tom Van Daele
- Expertise Unit Psychology, Technology and Society, Thomas More University of Applied Sciences, Mechelen, Belgium
| | - Carmen Moreno
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, Universidad Complutense de Madrid Facultad de Medicina, Madrid, Spain
| | - Per Carlbring
- Department of Psychology, Stockholm University, Stockholm, Sweden
| | - Ulrich W Ebner-Priemer
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
- mHealth Methods in Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, München, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck Institute of Psychiatry, Munich, Germany
| | - Heleen Riper
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam University Medical Centre, Duivendrecht, Netherlands
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Stephane Mouchabac
- Department of Psychiatry, Hôpital Saint-Antoine, Sorbonne Université, Paris, France
- Infrastructure for Clinical Research in Neurosciences (iCRIN), Brain Institute (ICM), INSERM, CNRS, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Paris, France
| | - John Torous
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrea Cipriani
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
- Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Oxford, UK
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Yang CY, Shiranthika C, Wang CY, Chen KW, Sumathipala S. Reinforcement learning strategies in cancer chemotherapy treatments: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107280. [PMID: 36529000 DOI: 10.1016/j.cmpb.2022.107280] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Cancer is one of the major causes of death worldwide and chemotherapies are the most significant anti-cancer therapy, in spite of the emerging precision cancer medicines in the last 2 decades. The growing interest in developing the effective chemotherapy regimen with optimal drug dosing schedule to benefit the clinical cancer patients has spawned innovative solutions involving mathematical modeling since the chemotherapy regimens are administered cyclically until the futility or the occurrence of intolerable adverse events. Thus, in this present work, we reviewed the emerging trends involved in forming a computational solution from the aspect of reinforcement learning. METHODS Initially, this survey in-depth focused on the details of the dynamic treatment regimens from a broad perspective and then narrowed down to inspirations from reinforcement learning that were advantageous to chemotherapy dosing, including both offline reinforcement learning and supervised reinforcement learning. RESULTS The insights established in the chemotherapy-planning problem associated with the Reinforcement Learning (RL) has been discussed in this study. It showed that the researchers were able to widen their perspectives in comprehending the theoretical basis, dynamic treatment regimens (DTR), use of the adaptive control on DTR, and the associated RL techniques. CONCLUSIONS This study reviewed the recent researches relevant to the topic, and highlighted the challenges, open questions, possible solutions, and future steps in inventing a realistic solution for the aforementioned problem.
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Affiliation(s)
- Chan-Yun Yang
- Department of Electrical Engineering, National Taipei University, New Taipei City, Taiwan
| | - Chamani Shiranthika
- Department of Electrical Engineering, National Taipei University, New Taipei City, Taiwan
| | - Chung-Yih Wang
- Department of Radiation Oncology, Cheng Hsin General Hospital, Taipei City, Taiwan
| | - Kuo-Wei Chen
- Section of Hematology and Oncology, Department of Internal Medicine, Cheng Hsin General Hospital, Taipei City, Taiwan.
| | - Sagara Sumathipala
- Faculty of Information Technology, University of Moratuwa, Katubedda, Moratuwa, Sri Lanka
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The promise of a model-based psychiatry: building computational models of mental ill health. Lancet Digit Health 2022; 4:e816-e828. [PMID: 36229345 PMCID: PMC9627546 DOI: 10.1016/s2589-7500(22)00152-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/05/2022] [Accepted: 07/27/2022] [Indexed: 11/07/2022]
Abstract
Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.
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