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Zafar F, Fakhare Alam L, Vivas RR, Wang J, Whei SJ, Mehmood S, Sadeghzadegan A, Lakkimsetti M, Nazir Z. The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review. Cureus 2024; 16:e56472. [PMID: 38638735 PMCID: PMC11025697 DOI: 10.7759/cureus.56472] [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] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain.
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Affiliation(s)
- Fabeha Zafar
- Internal Medicine, Dow University of Health Sciences (DUHS), Karachi, PAK
| | | | - Rafael R Vivas
- Nutrition, Food and Exercise Sciences, Florida State University College of Human Sciences, Tallahassee, USA
| | - Jada Wang
- Medicine, St. George's University, Brooklyn, USA
| | - See Jia Whei
- Internal Medicine, Sriwijaya University, Palembang, IDN
| | | | | | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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Haque MA, Gedara MLB, Nickel N, Turgeon M, Lix LM. The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis. BMC Med Inform Decis Mak 2024; 24:33. [PMID: 38308231 PMCID: PMC10836023 DOI: 10.1186/s12911-024-02416-3] [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: 06/19/2023] [Accepted: 01/03/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Smoking is a risk factor for many chronic diseases. Multiple smoking status ascertainment algorithms have been developed for population-based electronic health databases such as administrative databases and electronic medical records (EMRs). Evidence syntheses of algorithm validation studies have often focused on chronic diseases rather than risk factors. We conducted a systematic review and meta-analysis of smoking status ascertainment algorithms to describe the characteristics and validity of these algorithms. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. We searched articles published from 1990 to 2022 in EMBASE, MEDLINE, Scopus, and Web of Science with key terms such as validity, administrative data, electronic health records, smoking, and tobacco use. The extracted information, including article characteristics, algorithm characteristics, and validity measures, was descriptively analyzed. Sources of heterogeneity in validity measures were estimated using a meta-regression model. Risk of bias (ROB) in the reviewed articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. RESULTS The initial search yielded 2086 articles; 57 were selected for review and 116 algorithms were identified. Almost three-quarters (71.6%) of algorithms were based on EMR data. The algorithms were primarily constructed using diagnosis codes for smoking-related conditions, although prescription medication codes for smoking treatments were also adopted. About half of the algorithms were developed using machine-learning models. The pooled estimates of positive predictive value, sensitivity, and specificity were 0.843, 0.672, and 0.918 respectively. Algorithm sensitivity and specificity were highly variable and ranged from 3 to 100% and 36 to 100%, respectively. Model-based algorithms had significantly greater sensitivity (p = 0.006) than rule-based algorithms. Algorithms for EMR data had higher sensitivity than algorithms for administrative data (p = 0.001). The ROB was low in most of the articles (76.3%) that underwent the assessment. CONCLUSIONS Multiple algorithms using different data sources and methods have been proposed to ascertain smoking status in electronic health data. Many algorithms had low sensitivity and positive predictive value, but the data source influenced their validity. Algorithms based on machine-learning models for multiple linked data sources have improved validity.
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Affiliation(s)
- Md Ashiqul Haque
- Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | | | - Nathan Nickel
- Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Maxime Turgeon
- Department of Statistics, University of Manitoba, Winnipeg, MB, Canada
| | - Lisa M Lix
- Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
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Bednorz A, Mak JKL, Jylhävä J, Religa D. Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future. Clin Interv Aging 2023; 18:2171-2183. [PMID: 38152074 PMCID: PMC10752027 DOI: 10.2147/cia.s400887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/05/2023] [Indexed: 12/29/2023] Open
Abstract
Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and disease risk prediction. EMRs also offer a range of advantages in clinical practice, such as comprehensive medical records, streamlined communication with healthcare providers, remote data access, and rapid retrieval of test results, ultimately leading to increased efficiency, enhanced patient safety, and improved quality of care in gerontology, which includes benefits like reduced medication use and better patient history taking and physical examination assessments. The use of artificial intelligence (AI) and machine learning (ML) approaches on EMRs can further improve disease diagnosis, symptom classification, and support clinical decision-making. However, there are also challenges related to data quality, data entry errors, as well as the ethics and safety of using AI in healthcare. This article discusses the future of EMRs in gerontology and the application of AI and ML in clinical research. Ethical and legal issues surrounding data sharing and the need for healthcare professionals to critically evaluate and integrate these technologies are also emphasized. The article concludes by discussing the challenges related to the use of EMRs in research as well as in their primary intended use, the daily clinical practice.
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Affiliation(s)
- Adam Bednorz
- John Paul II Geriatric Hospital, Katowice, Poland
- Institute of Psychology, Humanitas Academy, Sosnowiec, Poland
| | - Jonathan K L Mak
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland
| | - Dorota Religa
- Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden
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Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatr 2023; 23:841. [PMID: 38087195 PMCID: PMC10717316 DOI: 10.1186/s12877-023-04477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/10/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area. METHODS This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used. RESULTS A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data. CONCLUSION The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.
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Affiliation(s)
- Ayushi Das
- International Institute for Population Sciences, Deonar, Mumbai, 400088, India
| | - Preeti Dhillon
- Department of Survey Research and Data Analytics, International Institute for Population Sciences, Deonar, Mumbai, 400088, India.
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Liang J, He Y, Xie J, Fan X, Liu Y, Wen Q, Shen D, Xu J, Gu S, Lei J. Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current research status and product conversion. J Biomed Inform 2023; 146:104480. [PMID: 37657713 DOI: 10.1016/j.jbi.2023.104480] [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: 02/16/2023] [Revised: 07/16/2023] [Accepted: 08/27/2023] [Indexed: 09/03/2023]
Abstract
BACKGROUND The use of Electronic Health Records is the most important milestone in the digitization and intelligence of the entire medical industry. AI can effectively mine the immense medical information contained in EHRs, potentially assist doctors in reducing many medical errors. OBJECTIVE This article aims to summarize the research status and trends in using AI to mine medical information from EHRs for the past thirteen years and investigate its information application. METHODS A systematic search was carried out in 5 databases, including Web of Science Core Collection and PubMed, to identify research using AI to mine medical information from EHRs for the past thirteen years. Furthermore, bibliometric and content analysis were used to explore the research hotspots and trends, and systematically analyze the conversion rate of research resources in this field. RESULTS A total of 631 articles were included and analyzed. The number of published articles has increased rapidly after 2017, with an average annual growth rate of 55.73%. The US (41.68%) and China (19.65%) publish the most articles, but there is a lack of international cooperation. The extraction of disease lesions is a hot topic at present, and the research topic is gradually shifting from disease risk grading to disease risk prediction. Classification (66%), and regress (15%) are the main implemented AI tasks. For AI algorithms, deep learning (31.70%), decision tree algorithms family (26.47%), and regression algorithms family (17.43%) are used most frequently. The funding rate for publications is 69.26%, and the input-output conversion rate is 21.05%. CONCLUSIONS Over the past decade, the use of AI to mine medical information from EHRs has been developing rapidly. However, it is necessary to strengthen international cooperation, improve EHRs data availability, focus on interpretable AI algorithms, and improve the resource conversion rate in future research.
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Affiliation(s)
- Jun Liang
- IT Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, Zhejiang Province, China; Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China; School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, Zhejiang Province, China
| | - Yunfan He
- Center for Health Policy Studies, School of Public Health, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Jun Xie
- Information Technology Center, West China Hospital of Sichuan University/Engineering Research Center of Medical Information Technology, Ministry of Education, Chengdu, Sichuan Province, China
| | - Xianming Fan
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Yiqi Liu
- Department of Infectious Disease, Center for Liver Disease, Peking University First Hospital, Beijing, China
| | - Qinglian Wen
- Department of Oncology, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China
| | - Dongxia Shen
- Editorial Department of Journal of Practical Oncology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Jie Xu
- IT Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
| | - Shuo Gu
- Hainan Provincial Center for Neurological Diseases, Department of Pediatric Neurosurgery of The First Affiliated Hospital, Hainan Medical University, Haikou, Hainan Province, China.
| | - Jianbo Lei
- Clinical Research Center, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, China; School of Medical Information and Engineering, SouthWest Medical University, Luzhou, Sichuan Province, China; Institute of Medical Technology, Health Science Center, Peking University, Beijing, China.
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Bhugra D, Smith A, Ventriglio A, Hermans MHM, Ng R, Javed A, Chumakov E, Kar A, Ruiz R, Oquendo M, Chisolm MS, Werneke U, Suryadevara U, Jibson M, Hobbs J, Castaldelli-Maia J, Nair M, Seshadri S, Subramanyam A, Patil N, Chandra P, Liebrenz M. World Psychiatric Association-Asian Journal of Psychiatry Commission on Psychiatric Education in the 21st century. Asian J Psychiatr 2023; 88:103739. [PMID: 37619422 DOI: 10.1016/j.ajp.2023.103739] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 08/10/2023] [Indexed: 08/26/2023]
Abstract
Psychiatric practice faces many challenges in the first quarter of 21st century. Society has transformed, as have training requirements and patient expectations, underlining an urgent need to look at educational programmes. Meanwhile, awareness has grown around psychiatric disorders and there are evolving workforce trends, with more women going to medical school and specialising in psychiatry. Trainee psychiatrists carry different expectations for work-life balance and are increasingly becoming conscious of their own mental health. A tendency to see health as a commodity and the litigious nature of society has elicited additional pressures for healthcare professionals. Cartesian mind-body dualism has created further complexity and this can often be frustrating for patients and care-partners alike. In many cultures across Asia and beyond, patients can present with physical symptoms to express underlying psychological distress with increasing physical investigations. Simultaneously, in various countries, a shift from asylums to community-based interventions and then home treatments have changed psychiatric care in remarkable ways. These changes have added to pressures faced by mental healthcare professionals. However, trainees and other mental healthcare professionals continue to receive similar training as they did a generation ago. The tensions and differences in ideology/orientation between different branches of psychiatry have made responses to patient needs challenging. Recognising that it is difficult to predict the future, this World Psychiatric Association-Asian Journal of Psychiatry Commission makes recommendations that could help institutions and individuals enhance psychiatric education. This Commission draws from existing resources and recent developments to propose a training framework for future psychiatrists.
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Affiliation(s)
- Dinesh Bhugra
- Institute of Psychiatry, Psychology and Neurosciences, Kings College, London SE5 8AF, UK.
| | - Alexander Smith
- Department of Forensic Psychiatry, University of Bern, Bern, Switzerland
| | | | | | - Roger Ng
- Secretary for Education, WPA, Geneva, Switzerland
| | - Afzal Javed
- World Psychiatric Association, Geneva. Switzerland. Fountain House, Lahore. Pakistan
| | - Egor Chumakov
- Department of Psychiatry & Addiction, St Petersburg State University, St Petersburg, Russia
| | - Anindya Kar
- Advanced Neuropsychiatry Institute, Kolkata, India
| | - Roxanna Ruiz
- University of Francisco Moaroquin, Guatemala City, Guatemala
| | - Maria Oquendo
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | | | - Ursula Werneke
- Department of Clinical Sciences, Psychiatry, Umeå University, Sunderby Research Unit, Umeå, Sweden
| | - Uma Suryadevara
- Geriatric Division, Department of Psychiatry, University of Florida, Gainesville, United States
| | - Michael Jibson
- Department of Psychiatry, University of Michigan, Ann Arbor, United States
| | - Jacqueline Hobbs
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, United States
| | | | - Muralidharan Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala 695011, India
| | - Shekhar Seshadri
- Department of Child Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka 560029, India
| | - Alka Subramanyam
- Department of Psychiatry, Topiwala Nair Medical College, Mumbai, Maharashtra 400008, India
| | - Nanasaheb Patil
- Department of Psychiatry, J.N. Medical College, Belgavi, Karnataka 590010, India
| | - Prabha Chandra
- Behavioral Sciences, National Institute of Mental Health and Neurosciences, Bengaluru 560029, India
| | - Michael Liebrenz
- Department of Forensic Psychiatry, University of Bern, Bern, Switzerland
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Bélanger E, Rosendaal N, Gutman R, Lake D, Santostefano CM, Meyers DJ, Gozalo PL. Identifying Medicare beneficiaries with Alzheimer's disease and related dementia using home health OASIS assessments. J Am Geriatr Soc 2023; 71:3229-3236. [PMID: 37358283 PMCID: PMC10592468 DOI: 10.1111/jgs.18487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/24/2023] [Accepted: 05/21/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND Home health services are an important site of care following hospitalization among Medicare beneficiaries, providing health assessments that can be leveraged to detect diagnoses that are not available in other data sources. In this work, we aimed to develop a parsimonious and accurate algorithm using home health outcome and assessment information set (OASIS) measures to identify Medicare beneficiaries with a diagnosis of Alzheimer's disease and related dementia (ADRD). METHODS We conducted a retrospective cohort study of Medicare beneficiaries with a complete OASIS start of care assessment in 2014, 2016, 2018, or 2019 to determine how well the items from various versions could identify those with an ADRD diagnosis by the assessment date. The prediction model was developed iteratively, comparing the performance of different models in terms of sensitivity, specificity, and accuracy of prediction, from a multivariable logistic regression model using clinically relevant variables, to regression models with all available variables and predictive modeling techniques, to estimate the best performing parsimonious model. RESULTS The most important predictors of having a diagnosis of ADRD by the start of care OASIS assessment were a prior discharge diagnosis of ADRD among those admitted from an inpatient setting, and frequently exhibiting symptoms of confusion. Results from the parsimonious model were consistent across the four annual cohorts and OASIS versions with high specificity (above 96%), but poor sensitivity (below 58%). The positive predictive value was high, over 87% across study years. CONCLUSIONS The proposed algorithm has high accuracy, requires a single OASIS assessment, is easy to implement without sophisticated statistical models, and can be used across four OASIS versions and in situations where claims are not available to identify individuals with a diagnosis of ADRD, including the growing population of Medicare Advantage beneficiaries.
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Affiliation(s)
- Emmanuelle Bélanger
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Nicole Rosendaal
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Roee Gutman
- Department of Biostatistics, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Derek Lake
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Christopher M Santostefano
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island, USA
| | - David J Meyers
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Pedro L Gozalo
- Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, Rhode Island, USA
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Pavon JM, Previll L, Woo M, Henao R, Solomon M, Rogers U, Olson A, Fischer J, Leo C, Fillenbaum G, Hoenig H, Casarett D. Machine learning functional impairment classification with electronic health record data. J Am Geriatr Soc 2023; 71:2822-2833. [PMID: 37195174 PMCID: PMC10524844 DOI: 10.1111/jgs.18383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/16/2023] [Accepted: 03/19/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment. METHODS We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome. RESULTS Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states. CONCLUSION A machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population-based strategy for identifying patients with poor functional status who need additional health resources.
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Affiliation(s)
- Juliessa M Pavon
- Department of Medicine/Division of Geriatrics, Duke University, Durham, North Carolina, USA
- Geriatric Research Education Clinical Center, Durham Veteran Affairs Health Care System, Durham, North Carolina, USA
- Claude D. Pepper Center, Duke University, Durham, North Carolina, USA
- Center for the Study of Aging and Human Development, Duke University, Durham, North Carolina, USA
| | - Laura Previll
- Department of Medicine/Division of Geriatrics, Duke University, Durham, North Carolina, USA
- Geriatric Research Education Clinical Center, Durham Veteran Affairs Health Care System, Durham, North Carolina, USA
- Center for the Study of Aging and Human Development, Duke University, Durham, North Carolina, USA
| | - Myung Woo
- AI Health, Duke University, Durham, North Carolina, USA
- Department of Medicine/Division of General Internal Medicine/Hospital Medicine, Duke University, Durham, North Carolina, USA
| | - Ricardo Henao
- AI Health, Duke University, Durham, North Carolina, USA
| | - Mary Solomon
- AI Health, Duke University, Durham, North Carolina, USA
| | - Ursula Rogers
- AI Health, Duke University, Durham, North Carolina, USA
| | - Andrew Olson
- AI Health, Duke University, Durham, North Carolina, USA
| | - Jonathan Fischer
- Department of Community and Family Medicine, Duke University, Durham, North Carolina, USA
| | - Christopher Leo
- Department of Medicine/Division of Geriatrics, Duke University, Durham, North Carolina, USA
- Department of Medicine/Division of General Internal Medicine/Hospital Medicine, Duke University, Durham, North Carolina, USA
| | - Gerda Fillenbaum
- Claude D. Pepper Center, Duke University, Durham, North Carolina, USA
- Center for the Study of Aging and Human Development, Duke University, Durham, North Carolina, USA
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA
| | - Helen Hoenig
- Department of Medicine/Division of Geriatrics, Duke University, Durham, North Carolina, USA
- Geriatric Research Education Clinical Center, Durham Veteran Affairs Health Care System, Durham, North Carolina, USA
- Claude D. Pepper Center, Duke University, Durham, North Carolina, USA
- Center for the Study of Aging and Human Development, Duke University, Durham, North Carolina, USA
- Physical Medicine & Rehabilitation Service, Durham Veteran Affairs Health Care System, Durham, North Carolina, USA
| | - David Casarett
- Department of Medicine/Division of General Internal Medicine/Palliative Care, Duke University, Durham, North Carolina, USA
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Eikelboom WS, Singleton EH, van den Berg E, de Boer C, Coesmans M, Goudzwaard JA, Vijverberg EGB, Pan M, Gouw C, Mol MO, Gillissen F, Fieldhouse JLP, Pijnenburg YAL, van der Flier WM, van Swieten JC, Ossenkoppele R, Kors JA, Papma JM. The reporting of neuropsychiatric symptoms in electronic health records of individuals with Alzheimer's disease: a natural language processing study. Alzheimers Res Ther 2023; 15:94. [PMID: 37173801 PMCID: PMC10176879 DOI: 10.1186/s13195-023-01240-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Neuropsychiatric symptoms (NPS) are prevalent in the early clinical stages of Alzheimer's disease (AD) according to proxy-based instruments. Little is known about which NPS clinicians report and whether their judgment aligns with proxy-based instruments. We used natural language processing (NLP) to classify NPS in electronic health records (EHRs) to estimate the reporting of NPS in symptomatic AD at the memory clinic according to clinicians. Next, we compared NPS as reported in EHRs and NPS reported by caregivers on the Neuropsychiatric Inventory (NPI). METHODS Two academic memory clinic cohorts were used: the Amsterdam UMC (n = 3001) and the Erasmus MC (n = 646). Patients included in these cohorts had MCI, AD dementia, or mixed AD/VaD dementia. Ten trained clinicians annotated 13 types of NPS in a randomly selected training set of n = 500 EHRs from the Amsterdam UMC cohort and in a test set of n = 250 EHRs from the Erasmus MC cohort. For each NPS, a generalized linear classifier was trained and internally and externally validated. Prevalence estimates of NPS were adjusted for the imperfect sensitivity and specificity of each classifier. Intra-individual comparison of the NPS classified in EHRs and NPS reported on the NPI were conducted in a subsample (59%). RESULTS Internal validation performance of the classifiers was excellent (AUC range: 0.81-0.91), but external validation performance decreased (AUC range: 0.51-0.93). NPS were prevalent in EHRs from the Amsterdam UMC, especially apathy (adjusted prevalence = 69.4%), anxiety (adjusted prevalence = 53.7%), aberrant motor behavior (adjusted prevalence = 47.5%), irritability (adjusted prevalence = 42.6%), and depression (adjusted prevalence = 38.5%). The ranking of NPS was similar for EHRs from the Erasmus MC, although not all classifiers obtained valid prevalence estimates due to low specificity. In both cohorts, there was minimal agreement between NPS classified in the EHRs and NPS reported on the NPI (all kappa coefficients < 0.28), with substantially more reports of NPS in EHRs than on NPI assessments. CONCLUSIONS NLP classifiers performed well in detecting a wide range of NPS in EHRs of patients with symptomatic AD visiting the memory clinic and showed that clinicians frequently reported NPS in these EHRs. Clinicians generally reported more NPS in EHRs than caregivers reported on the NPI.
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Affiliation(s)
- Willem S Eikelboom
- Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, the Netherlands.
| | - Ellen H Singleton
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Esther van den Berg
- Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Casper de Boer
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Michiel Coesmans
- Department of Psychiatry, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Jeannette A Goudzwaard
- Department of Internal Medicine, Section of Geriatrics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Everard G B Vijverberg
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Michel Pan
- Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Cornalijn Gouw
- Department of Psychiatry, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Merel O Mol
- Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Freek Gillissen
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Jay L P Fieldhouse
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Yolande A L Pijnenburg
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - John C van Swieten
- Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, the Netherlands
| | - Rik Ossenkoppele
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam University Medical Centers, Amsterdam, the Netherlands
- Clinical Memory Research Unit, Lund University, Malmö, Sweden
| | - Jan A Kors
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Janne M Papma
- Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, the Netherlands
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Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E. Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study. JOURNAL OF BUSINESS RESEARCH 2023; 160:113806. [PMID: 36895308 PMCID: PMC9981538 DOI: 10.1016/j.jbusres.2023.113806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 01/18/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.
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Affiliation(s)
- Miguel Ortiz-Barrios
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Sebastián Arias-Fonseca
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla 080002, Colombia
| | - Alessio Ishizaka
- NEOMA Business School, 1 rue du Maréchal Juin, Mont-Saint-Aignan 76130, France
| | - Maria Barbati
- Department of Economics, University Ca' Foscari, Cannaregio 873, Fondamenta San Giobbe, 30121 Venice, Italy
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11
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Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, Turner K. Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. Comput Biol Med 2023; 155:106649. [PMID: 36805219 DOI: 10.1016/j.compbiomed.2023.106649] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/04/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. METHODOLOGY After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULT AND DISCUSSION EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. CONCLUSION We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.
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Affiliation(s)
- Elias Hossain
- School of Engineering & Physical Sciences, North South University, Dhaka 1229, Bangladesh.
| | - Rajib Rana
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Niall Higgins
- School of Management and Enterprise, University of Southern Queensland, Darling Heights QLD 4350, Australia; School of Nursing, Queensland University of Technology, Kelvin Grove, Brisbane, QLD 4000, Australia; Metro North Mental Health, Herston QLD 4029, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Prabal Datta Barua
- School of Business, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Anthony R Pisani
- Center for the Study and Prevention of Suicide, University of Rochester, Rochester, NY, United States
| | - Kathryn Turner
- School of Nursing, Queensland University of Technology, Kelvin Grove, Brisbane, QLD 4000, Australia
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