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Adams SW, Layne CM, Contractor AA, Allwood MA, Armour C, Inslicht SS, Maguen S. The Middle-Out Approach to reconceptualizing, assessing, and analyzing traumatic stress reactions. J Trauma Stress 2023. [PMID: 38049964 DOI: 10.1002/jts.23005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/15/2023] [Accepted: 11/15/2023] [Indexed: 12/06/2023]
Abstract
Alternative models of traumatic stress and broader psychopathology have been proposed to address issues of heterogeneity, comorbidity, clinical utility, and equitable representation. However, systematic and practical methods and guidelines to organize and apply these models remain scarce. The Middle-Out Approach is a novel, integrative, contextually informed framework for organizing and applying existing empirical methods to evaluate current and alternative traumatic stress reactions. Rather than beginning to identify traumatic stress reactions from the top-down (i.e., disorder-first approach) or bottom-up (i.e., symptom-first approach), constructs are evaluated from the middle out (i.e., presentation-first approach), unconstrained by higher-order disorders or lower-order diagnostic symptoms. This approach provides innovation over previous methods at multiple levels, including the conceptualization of traumatic stress reactions as well as the type of assessments and data sources used and how they are used in statistical analyses. Conceptualizations prioritize the identification of middle-order phenotypes, representing person-centered clinical presentations, which are informed by the integration of multidimensional, transdiagnostic, and multimodal (e.g., psychosocial, physiological) assessments and/or data sources. Integrated data are then analyzed concurrently using person-centered statistical models to identify precise, discrete, and representative health outcomes within broader heterogeneous samples. Subsequent variable-centered analyses are then used to identify culturally sensitive and contextually informed correlates of phenotypes, their clinical utility, and the differential composition within and between broader traumatic stress reactions. Examples from the moral injury literature are used to illustrate practical applications that may increase clinical utility and the accurate representation of health outcomes for diverse individuals and communities.
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Affiliation(s)
- Shane W Adams
- Mental Illness Research Education and Clinical Center (MIRECC), VA San Francisco Health Care System, San Francisco, California, USA
- Department of Psychiatry, University of California-San Francisco, San Francisco, California, USA
| | - Christopher M Layne
- College of Psychology, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | | | - Maureen A Allwood
- Department of Psychology, John Jay College of Criminal Justice-City University of New York, New York, New York, USA
| | - Chérie Armour
- School of Psychology, Queens University Belfast, Belfast, Northern Ireland, UK
| | - Sabra S Inslicht
- Department of Psychiatry, University of California-San Francisco, San Francisco, California, USA
- Mental Health Service, VA San Francisco Health Care System, San Francisco, California, USA
| | - Shira Maguen
- Department of Psychiatry, University of California-San Francisco, San Francisco, California, USA
- Mental Health Service, VA San Francisco Health Care System, San Francisco, California, USA
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Chandler C, Diaz‐Asper C, Turner RS, Reynolds B, Elvevåg B. An explainable machine learning model of cognitive decline derived from speech. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12516. [PMID: 38155915 PMCID: PMC10752754 DOI: 10.1002/dad2.12516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023]
Abstract
INTRODUCTION Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders. METHODS Speech collected over the telephone from 91 older participants who were cognitively healthy (n = 29) or had diagnoses of AD (n = 30) or amnestic MCI (aMCI; n = 32) was analyzed with multimodal natural language and speech processing methods. An explainable ensemble decision tree classifier for the multiclass prediction of cognitive decline was created. RESULTS This approach was 75% accurate overall-an improvement over traditional speech-based screening tools and a unimodal language-based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data. DISCUSSION This work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies. Highlights Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening.Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls.AI predictions of cognitive decline are more accurate than experts and standard tools.The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.
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Affiliation(s)
- Chelsea Chandler
- Institute of Cognitive ScienceUniversity of ColoradoBoulderColoradoUSA
| | | | - Raymond S. Turner
- Department of NeurologyGeorgetown UniversityWashingtonDistrict of ColumbiaUSA
| | - Brigid Reynolds
- Department of NeurologyGeorgetown UniversityWashingtonDistrict of ColumbiaUSA
| | - Brita Elvevåg
- Department of Clinical MedicineUniversity of Tromsø – the Arctic University of NorwayTromsøNorway
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Jin KW, Li Q, Xie Y, Xiao G. Artificial intelligence in mental healthcare: an overview and future perspectives. Br J Radiol 2023; 96:20230213. [PMID: 37698582 PMCID: PMC10546438 DOI: 10.1259/bjr.20230213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 09/13/2023] Open
Abstract
Artificial intelligence is disrupting the field of mental healthcare through applications in computational psychiatry, which leverages quantitative techniques to inform our understanding, detection, and treatment of mental illnesses. This paper provides an overview of artificial intelligence technologies in modern mental healthcare and surveys recent advances made by researchers, focusing on the nascent field of digital psychiatry. We also consider the ethical implications of artificial intelligence playing a greater role in mental healthcare.
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Affiliation(s)
| | - Qiwei Li
- Department of Mathemaical Sciences, The University of Texas at Dallas, Richardson, Texas, United States
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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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5
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Castro Martínez JC, Santamaría-García H. Understanding mental health through computers: An introduction to computational psychiatry. Front Psychiatry 2023; 14:1092471. [PMID: 36824671 PMCID: PMC9941647 DOI: 10.3389/fpsyt.2023.1092471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.
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Affiliation(s)
- Juan Camilo Castro Martínez
- Departamento de Psiquiatría y Salud Mental, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Hernando Santamaría-García
- Ph.D. Programa de Neurociencias, Departamento de Psiquiatría y Salud Mental, Pontificia Universidad Javeriana, Bogotá, Colombia
- Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Global Brain Health Institute, University of California, San Francisco – Trinity College Dublin, San Francisco, CA, United States
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Del Casale A, Sarli G, Bargagna P, Polidori L, Alcibiade A, Zoppi T, Borro M, Gentile G, Zocchi C, Ferracuti S, Preissner R, Simmaco M, Pompili M. Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry. Curr Neuropharmacol 2023; 21:2395-2408. [PMID: 37559539 PMCID: PMC10616924 DOI: 10.2174/1570159x21666230808170123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 08/11/2023] Open
Abstract
Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.
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Affiliation(s)
- Antonio Del Casale
- Department of Dynamic and Clinical Psychology and Health Studies, Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Giuseppe Sarli
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Paride Bargagna
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Lorenzo Polidori
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Alessandro Alcibiade
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Teodolinda Zoppi
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Marina Borro
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Laboratory and Advanced Molecular Diagnostics, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Giovanna Gentile
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Laboratory and Advanced Molecular Diagnostics, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Clarissa Zocchi
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Stefano Ferracuti
- Department of Human Neuroscience, Faculty of Medicine and Dentistry, Sapienza University, Unit of Risk Management, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Robert Preissner
- Institute of Physiology and Science-IT, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Philippstrasse 12, 10115, Berlin, Germany
| | - Maurizio Simmaco
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Laboratory and Advanced Molecular Diagnostics, ‘Sant’Andrea’ University Hospital, Rome, Italy
| | - Maurizio Pompili
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Faculty of Medicine and Psychology, Sapienza University; Unit of Psychiatry, ‘Sant’Andrea’ University Hospital, Rome, Italy
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Bhardwaj A. Promise and Provisos of Artificial Intelligence and Machine Learning in Healthcare. J Healthc Leadersh 2022; 14:113-118. [PMID: 35898671 PMCID: PMC9309280 DOI: 10.2147/jhl.s369498] [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: 04/19/2022] [Accepted: 07/13/2022] [Indexed: 11/29/2022] Open
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) promise to transform all facets of medicine. Expected changes include more effective clinical triage, enhanced accuracy of diagnostic interpretations, improved therapeutic interventions, augmented workflow algorithms, streamlined data collection and processing, more precise disease prognostication, newer pharmacotherapies, and ameliorated genome interpretation. However, many caveats remain. Reliability of input data, interpretation of output data, data proprietorship, consumer privacy, and liability issues due to potential for data breaches will all have to be addressed. Of equal concern will be decreased human interaction in clinical care, patient satisfaction, affordability, and skepticism regarding cost-benefit. This descriptive literature-based treatise expounds on the promise and provisos associated with the anticipated import of AI and ML into all domains of medicine and healthcare in the very near future.
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Affiliation(s)
- Anish Bhardwaj
- Departments of Neurology, Neurosurgery, Neuroscience, Cell Biology and Anatomy, University of Texas Medical Branch (UTMB), Galveston, TX, USA
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8
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Tutun S, Johnson ME, Ahmed A, Albizri A, Irgil S, Yesilkaya I, Ucar EN, Sengun T, Harfouche A. An AI-based Decision Support System for Predicting Mental Health Disorders. INFORMATION SYSTEMS FRONTIERS : A JOURNAL OF RESEARCH AND INNOVATION 2022; 25:1261-1276. [PMID: 35669335 PMCID: PMC9142346 DOI: 10.1007/s10796-022-10282-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/21/2022] [Indexed: 05/27/2023]
Abstract
Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.
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Affiliation(s)
- Salih Tutun
- Washington University in St. Louis, St. Louis, MO USA
| | | | | | | | - Sedat Irgil
- Guven Private Health Laboratory, Guven, Turkey
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Parziale A, Mascalzoni D. Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem. Front Psychiatry 2022; 13:873392. [PMID: 35757212 PMCID: PMC9225201 DOI: 10.3389/fpsyt.2022.873392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Psychiatric research traditionally relies on subjective observation, which is time-consuming and labor-intensive. The widespread use of digital devices, such as smartphones and wearables, enables the collection and use of vast amounts of user-generated data as "digital biomarkers." These tools may also support increased participation of psychiatric patients in research and, as a result, the production of research results that are meaningful to them. However, sharing mental health data and research results may expose patients to discrimination and stigma risks, thus discouraging participation. To earn and maintain participants' trust, the first essential requirement is to implement an appropriate data governance system with a clear and transparent allocation of data protection duties and responsibilities among the actors involved in the process. These include sponsors, investigators, operators of digital tools, as well as healthcare service providers and biobanks/databanks. While previous works have proposed practical solutions to this end, there is a lack of consideration of positive data protection law issues in the extant literature. To start filling this gap, this paper discusses the GDPR legal qualifications of controller, processor, and joint controllers in the complex ecosystem unfolded by the integration of digital biomarkers in psychiatric research, considering their implications and proposing some general practical recommendations.
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Ćosić K, Popović S, Šarlija M, Kesedžić I, Gambiraža M, Dropuljić B, Mijić I, Henigsberg N, Jovanovic T. AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients. Front Psychol 2021; 12:782866. [PMID: 35027902 PMCID: PMC8751545 DOI: 10.3389/fpsyg.2021.782866] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/02/2021] [Indexed: 12/30/2022] Open
Abstract
The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.
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Affiliation(s)
- Krešimir Ćosić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Siniša Popović
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Marko Šarlija
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Ivan Kesedžić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Mate Gambiraža
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Branimir Dropuljić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Igor Mijić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Neven Henigsberg
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, United States
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Renn BN, Schurr M, Zaslavsky O, Pratap A. Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care. Front Psychiatry 2021; 12:734909. [PMID: 34867524 PMCID: PMC8634654 DOI: 10.3389/fpsyt.2021.734909] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/07/2021] [Indexed: 11/26/2022] Open
Abstract
Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clinical science, and address issues of ethics, privacy, and trust.
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Affiliation(s)
- Brenna N Renn
- Department of Psychology, University of Nevada, Las Vegas, NV, United States
| | - Matthew Schurr
- Department of Psychology, University of Nevada, Las Vegas, NV, United States
| | - Oleg Zaslavsky
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, WA, United States
| | - Abhishek Pratap
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON, Canada.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.,Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study. Indian Heart J 2020; 73:109-113. [PMID: 33714394 PMCID: PMC7683295 DOI: 10.1016/j.ihj.2020.11.145] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 11/17/2020] [Accepted: 11/20/2020] [Indexed: 11/22/2022] Open
Abstract
Background There is no large contemporary data from India to see the prevalence of burnout in HCWs in covid era. Burnout and mental stress is associated with electrocardiographic changes detectable by artificial intelligence (AI). Objective The present study aims to estimate the prevalence of burnout in HCWs in COVID-19 era using Mini Z-scale and to develop predictive AI model to detect burnout in HCWs in COVID-19 era. Methods This is an observational and cross-sectional study to evaluate the presence of burnout in HCWs in academic tertiary care centres of North India in the COVID-19 era. At least 900 participants will be enrolled in this study from four leading premier government-funded/public-private centres of North India. Each study centre will be asked to recruit HCWs by approaching them through various listed ways for participation in the study. Interested participants after initial screening and meeting the eligibility criteria, will be asked to fill the questionnaire (having demographic and work related with Mini Z questionnaire) to assess burnout. The healthcare workers will include physicians at all levels of training, nursing staff and paramedical staff who are involved directly or indirectly in COVID-19 care. The analysis of the raw electrocardiogram (ECG) data and development of algorithm using convolutional neural networks (CNN) will be done by experts. Conclusions In Summary, we propose that ECG data generated from the people with burnout can be utilized to develop AI-enabled model to predict the presence of stress and burnout in HCWs in COVID-19 era.
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Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders. Comput Struct Biotechnol J 2020; 18:2535-2546. [PMID: 33033576 PMCID: PMC7522539 DOI: 10.1016/j.csbj.2020.09.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/06/2020] [Accepted: 09/07/2020] [Indexed: 12/30/2022] Open
Abstract
Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.
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Holmlund TB, Chandler C, Foltz PW, Cohen AS, Cheng J, Bernstein JC, Rosenfeld EP, Elvevåg B. Applying speech technologies to assess verbal memory in patients with serious mental illness. NPJ Digit Med 2020; 3:33. [PMID: 32195368 PMCID: PMC7066153 DOI: 10.1038/s41746-020-0241-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 02/13/2020] [Indexed: 12/21/2022] Open
Abstract
Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated scoring, and repeated self-administration via smart devices. One hundred and four adults participated, comprising 25 patients with serious mental illness and 79 healthy volunteers. The study design was successful with high quality speech recordings produced to 92% of prompts (Patients: 86%, Healthy: 96%). The story recalls were both transcribed and scored by humans, and scores generated using natural language processing on transcriptions were comparable to human ratings (R = 0.83, within the range of human-to-human correlations of R = 0.73-0.89). A fully automated approach that scored transcripts generated by automatic speech recognition produced comparable and accurate scores (R = 0.82), with very high correlation to scores derived from human transcripts (R = 0.99). This study demonstrates the viability of leveraging speech technologies to facilitate the frequent assessment of verbal memory for clinical monitoring purposes in psychiatry.
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Affiliation(s)
| | | | - Peter W. Foltz
- University of Colorado Boulder, Boulder, CO USA
- Pearson PLC, London, England
| | | | - Jian Cheng
- Analytic Measures Inc, Palo Alto, CA USA
| | | | | | - Brita Elvevåg
- UiT The Arctic University of Norway, Tromsø, Norway
- Norwegian Centre for eHealth Research, Tromsø, Norway
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