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Koochakpour K, Nytrø Ø, Leventhal BL, Sverre Westbye O, Brox Røst T, Koposov R, Frodl T, Clausen C, Stien L, Skokauskas N. A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services. Int J Med Inform 2024; 188:105479. [PMID: 38761460 DOI: 10.1016/j.ijmedinf.2024.105479] [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: 03/03/2023] [Revised: 06/16/2023] [Accepted: 05/08/2024] [Indexed: 05/20/2024]
Abstract
OBJECTIVE Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). Our secondary objective is to identify the potential for data analysis in different clinical services and functions in which data-driven decision aids can be useful. MATERIALS AND METHODS We searched related studies in Science Direct and PubMed from 2018 to 2023(Jun), and also in ACM (Association for Computing Machinery) Digital Library, DBLP (Database systems and Logic Programming), and Google Scholar from 2018 to 2021. We have reviewed 39 studies and extracted types of analytical methods, information content, and information sources for decision-making. RESULTS In order to compare studies, we developed a framework for characterizing health services, functions, and data features. Most data sets in reviewed studies were small, with a median of 1,176 patients and 46,503 record entries. Structured data was used for all studies except two that used textual clinical notes. Most studies used supervised classification and regression. Service and situation-specific data analysis dominated among the studies, only two studies used temporal, or process features from the patient data. This paper presents and summarizes the utility, but not quality, of the studies according to the care situations and care providers to identify service functions where data-driven decision aids may be relevant. CONCLUSIONS Frameworks identifying services, functions, and care processes are necessary for characterizing and comparing electronic health record (EHR) data analysis studies. The majority of studies use features related to diagnosis and assessment and correspondingly have utility for intervention planning and follow-up. Profiling the disease severity of referred patients is also an important application area.
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Affiliation(s)
- Kaban Koochakpour
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
| | - Øystein Nytrø
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Department of Computer Science, The Arctic University of Norway (UiT), Tromsø, Norway
| | | | - Odd Sverre Westbye
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Department of Child and Adolescent Psychiatry, St. Olav's University Hospital, Trondheim, Norway
| | | | - Roman Koposov
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU), The Arctic University of Norway (UiT), Tromsø, Norway
| | - Thomas Frodl
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Carolyn Clausen
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Line Stien
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Norbert Skokauskas
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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Ciharova M, Amarti K, van Breda W, Peng X, Lorente-Català R, Funk B, Hoogendoorn M, Koutsouleris N, Fusar-Poli P, Karyotaki E, Cuijpers P, Riper H. Use of Machine-Learning Algorithms Based on Text, Audio and Video Data in the Prediction of Anxiety and Post-Traumatic Stress in General and Clinical Populations: A Systematic Review. Biol Psychiatry 2024:S0006-3223(24)01362-3. [PMID: 38866173 DOI: 10.1016/j.biopsych.2024.06.002] [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: 11/27/2023] [Revised: 05/24/2024] [Accepted: 06/04/2024] [Indexed: 06/14/2024]
Abstract
Research in machine-learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state-of-the-art is missing. Moreover, individual studies often target ML experts, and may overlook potential clinical implications of their findings. In a narrative accessible to mental health professionals, we present a systematic review, conducted in 5 psychology and 2 computer-science databases. We included 128 studies assessing the predictive power of ML algorithms using text, audio, and/or video data in the prediction of anxiety and post-traumatic stress (PTSD). Most studies (n = 87) aimed at predicting anxiety, the remainder (n = 41) focused on PTSD. They were mostly published since 2019, in computer science journals, and tested algorithms using text (n = 72), as opposed to audio or video. They focused mainly on general populations (n = 92), less on laboratory experiments (n = 23) or clinical populations (n = 13). Methodological quality varied, as did reported metrics of the predictive power, hampering comparison across studies. Two thirds of studies, focusing on both disorders, reported acceptable to very good predictive power (including high-quality studies only). Results of 33 studies were uninterpretable, mainly due to missing information. Research into ML algorithms using natural behavior is in its infancy, but shows potential to contribute to diagnostics of mental disorders, such as anxiety and PTSD, in the future, if standardization of methods, reporting of results, and research in clinical populations are improved.
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Affiliation(s)
- Marketa Ciharova
- Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Black Dog Institute, University of New South Wales, Sydney, NSW, Australia.
| | - Khadicha Amarti
- Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ward van Breda
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Xianhua Peng
- Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Department of Methodology and Statistics, Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, the Netherlands
| | - Rosa Lorente-Català
- Department of Basic and Clinical Psychology and Psychobiology, Universitat Jaume I, Castellon, Spain
| | - Burkhardt Funk
- Institute of Information Systems, Leuphana University, Lüneburg, Germany
| | - Mark Hoogendoorn
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom; Max Planck Institute of Psychiatry, Munich, Germany
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, United Kingdom
| | - Eirini Karyotaki
- Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; WHO Collaborating Center for Research and Dissemination of Psychological Interventions, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Pim Cuijpers
- Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; WHO Collaborating Center for Research and Dissemination of Psychological Interventions, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Babeș-Bolyai University, International Institute for Psychotherapy, Cluj-Napoca, Romania
| | - Heleen Riper
- Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
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Quillivic R, Gayraud F, Auxéméry Y, Vanni L, Peschanski D, Eustache F, Dayan J, Mesmoudi S. Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning. Sci Rep 2024; 14:12468. [PMID: 38816468 PMCID: PMC11139884 DOI: 10.1038/s41598-024-61557-7] [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: 12/13/2023] [Accepted: 05/07/2024] [Indexed: 06/01/2024] Open
Abstract
Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5-11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.
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Affiliation(s)
- Robin Quillivic
- PSL-EPHE, Paris, France.
- ISCPIF, Institut des Systèmes Complexes, Paris île-de-France, France.
| | - Frédérique Gayraud
- Laboratoire dynamique du langage, UMR 5596, CNRS, université ́ Lyon-II, Lyon, France
| | - Yann Auxéméry
- Centre Hospitalier de Jury-les-Metz, centre de réhabilitation pour adultes, Metz, France
- UMR 1319 Inspiire, INSERM, Université de Lorraine, 9 avenue de la forêt de Haye, Nancy, France
| | - Laurent Vanni
- CNRS, UMR 7320 : Bases, Corpus, Langage, Nice, France
| | - Denis Peschanski
- Université PARIS 1 Panthéon-Sorbonne, Paris, France
- CNRS, CESSP, UMR 8209, Paris, France
| | - Francis Eustache
- PSL-EPHE, Paris, France
- INSERM, NIMH U1077, Caen, France
- UNICAEN, Caen, France
| | - Jacques Dayan
- PSL-EPHE, Paris, France
- INSERM, NIMH U1077, Caen, France
- UNICAEN, Caen, France
- CHU de Rennes, Rennes, France
| | - Salma Mesmoudi
- PSL-EPHE, Paris, France
- ISCPIF, Institut des Systèmes Complexes, Paris île-de-France, France
- Université PARIS 1 Panthéon-Sorbonne, Paris, France
- CNRS, CESSP, UMR 8209, Paris, France
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Wang J, Ouyang H, Jiao R, Cheng S, Zhang H, Shang Z, Jia Y, Yan W, Wu L, Liu W. The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis. NPJ Digit Med 2024; 7:121. [PMID: 38724610 PMCID: PMC11082170 DOI: 10.1038/s41746-024-01117-5] [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: 09/23/2023] [Accepted: 04/23/2024] [Indexed: 05/12/2024] Open
Abstract
Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.
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Affiliation(s)
- Jing Wang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
| | - Hui Ouyang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
| | - Runda Jiao
- Graduate School, PLA General Hospital, 100853, Beijing, China
| | - Suhui Cheng
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
| | - Haiyan Zhang
- Department of Health Care, The First Affiliated Hospital of Naval Medical University, 200433, Shanghai, China
| | - Zhilei Shang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
| | - Yanpu Jia
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
| | - Wenjie Yan
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China
| | - Lili Wu
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China.
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China.
| | - Weizhi Liu
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China.
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, 200433, Shanghai, China.
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Jia Y, Yang B, Yang Y, Zheng W, Wang L, Huang C, Lu J, Chen N. Application of machine learning techniques in the diagnostic approach of PTSD using MRI neuroimaging data: A systematic review. Heliyon 2024; 10:e28559. [PMID: 38571633 PMCID: PMC10988057 DOI: 10.1016/j.heliyon.2024.e28559] [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: 10/07/2023] [Revised: 03/20/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
Background At present, the diagnosis of post-traumatic stress disorder(PTSD) mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging(DTI)are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis. Objectives According to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls). Methods We searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors. Results All ML techniques have an diagnostic accuracy rate above 70%,and support vector machine(SVM) are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis. Conclusion In contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.
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Affiliation(s)
- Y.L. Jia
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, 100053, China
| | - B.N. Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, 100053, China
| | - Y.H. Yang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, 100053, China
| | - W.M. Zheng
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, 100053, China
| | - L. Wang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, 100053, China
| | - C.Y. Huang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, 100053, China
| | - J. Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, 100053, China
| | - N. Chen
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, 100053, China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, 100053, China
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Wang J, Ouyang H, Jiao R, Zhang H, Cheng S, Shang Z, Jia Y, Yan W, Wu L, Liu W. Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis. Digit Health 2024; 10:20552076241239238. [PMID: 38495863 PMCID: PMC10943756 DOI: 10.1177/20552076241239238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/27/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classification of people with PTSD. Methods and analysis In pursuit of pertinent studies, we will search both English and Chinese databases from January 2000 to May 2022. Two researchers will independently conduct screening, extract data and assess study quality. We intend to employ the assessment framework introduced by Luis Francisco Ramos-Lima in 2020 for quality evaluation. Rate, standard error and 95% CIs will be utilized for effect size measurement. A Cochran's Q test will be applied to assess heterogeneity. Subgroup and sensitivity analysis will further elucidate the source of heterogeneity and funnel plots and Egger's test will detect publication bias. Ethics and dissemination This systematic review and meta-analysis does not encompass patient interactions or engagements with healthcare providers. The outcomes of this research will be disseminated through scholarly channels, including presentations at scientific conferences and publications in peer-reviewed journals.PROSPERO registration number CRD42023342042.
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Affiliation(s)
- Jing Wang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Hui Ouyang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Runda Jiao
- Graduate School, PLA General Hospital, Beijing, China
| | - Haiyan Zhang
- Department of Health Care, The First Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Suhui Cheng
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Zhilei Shang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Yanpu Jia
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Wenjie Yan
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Lili Wu
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Weizhi Liu
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Papini S, Iturralde E, Lu Y, Greene JD, Barreda F, Sterling SA, Liu VX. Development and validation of a machine learning model using electronic health records to predict trauma- and stressor-related psychiatric disorders after hospitalization with sepsis. Transl Psychiatry 2023; 13:400. [PMID: 38114475 PMCID: PMC10730505 DOI: 10.1038/s41398-023-02699-6] [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: 04/25/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 12/21/2023] Open
Abstract
A significant minority of individuals develop trauma- and stressor-related disorders (TSRD) after surviving sepsis, a life-threatening immune response to infections. Accurate prediction of risk for TSRD can facilitate targeted early intervention strategies, but many existing models rely on research measures that are impractical to incorporate to standard emergency department workflows. To increase the feasibility of implementation, we developed models that predict TSRD in the year after survival from sepsis using only electronic health records from the hospitalization (n = 217,122 hospitalizations from 2012-2015). The optimal model was evaluated in a temporally independent prospective test sample (n = 128,783 hospitalizations from 2016-2017), where patients in the highest-risk decile accounted for nearly one-third of TSRD cases. Our approach demonstrates that risk for TSRD after sepsis can be stratified without additional assessment burden on clinicians and patients, which increases the likelihood of model implementation in hospital settings.
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Affiliation(s)
- Santiago Papini
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
- Department of Psychology, University of Hawai'i at Mānoa, Honolulu, HI, USA.
| | - Esti Iturralde
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Yun Lu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - John D Greene
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Fernando Barreda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Stacy A Sterling
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Vincent X Liu
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
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Haller OC, King TZ, Mathur M, Turner JA, Wang C, Jovanovic T, Stevens JS, Fani N. White matter predictors of PTSD: Testing different machine learning models in a sample of Black American women. J Psychiatr Res 2023; 168:256-262. [PMID: 37922600 PMCID: PMC10841705 DOI: 10.1016/j.jpsychires.2023.10.046] [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: 04/28/2023] [Revised: 08/21/2023] [Accepted: 10/25/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Machine learning neuroimaging studies of posttraumatic stress disorder (PTSD) show promise for identifying neurobiological signatures of PTSD. However, studies to date, have largely evaluated a single machine learning approach, and few studies have examined white matter microstructure as a predictor of PTSD. Further, individuals from minoritized racial groups, specifically, Black individuals, who experience disproportionate trauma frequency, and have relatively higher rates of PTSD, have been underrepresented in these studies. We used four different machine learning models to test white matter microstructure classifiers of PTSD in a sample of trauma-exposed Black American women with and without PTSD. METHOD Participants included 45 Black women with PTSD and 89 trauma-exposed controls recruited from an ongoing trauma study. Current PTSD presence was estimated using the Clinician-Administered PTSD Scale. Average fractional anisotropy of 53 white matter tracts served as input features. Additional exploratory analysis incorporated estimates of interpersonal and structural racism exposure. Classification models included linear support vector machine, radial basis function support vector machine, multilayer perceptron, and random forest. RESULTS Performance varied notably between models. With white matter features along, linear support vector machine demonstrated the best model fit and reached an average AUC = 0.643. Inclusion of estimates of exposure to racism increased linear support vector machine performance (AUC = 0.808). CONCLUSIONS White matter microstructure had limited ability to predict PTSD presence in this sample. These results may indicate that the relationship between white matter microstructure and PTSD may be nuanced across race and gender spectrums.
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Affiliation(s)
- Olivia C Haller
- Department of Psychology, Georgia State University, Atlanta, GA, USA.
| | - Tricia Z King
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Mrinal Mathur
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Chenyang Wang
- Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
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10
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Fedele E, Trousset V, Schalk T, Oliero J, Fovet T, Lefevre T. Identification of Psycho-Socio-Judicial Trajectories and Factors Associated With Posttraumatic Stress Disorder in People Over 15 Years of Age Who Recently Reported Sexual Assault to a Forensic Medical Center: Protocol for a Multicentric Prospective Study Using Mixed Methods and Artificial Intelligence. JMIR Res Protoc 2023; 12:e46652. [PMID: 37843900 PMCID: PMC10616743 DOI: 10.2196/46652] [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: 02/21/2023] [Revised: 06/29/2023] [Accepted: 07/31/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Sexual assault (SA) can lead to a range of adverse effects on physical, sexual, and mental health, as well as on one's social life, financial stability, and overall quality of life. However, not all people who experience SA will develop negative functional outcomes. Various risk and protective factors can influence psycho-socio-judicial trajectories. However, how these factors influence trauma adaptation and the onset of early posttraumatic stress disorder (PTSD) is not always clear. OBJECTIVE Guided by an ecological framework, this project has 3 primary objectives: (1) to describe the 1-year psycho-socio-judicial trajectories of individuals recently exposed to SA who sought consultation with a forensic practitioner; (2) to identify predictive factors for the development of PTSD during the initial forensic examination using artificial intelligence; and (3) to explore the perceptions, needs, and experiences of individuals who have been sexually assaulted. METHODS This longitudinal multicentric cohort study uses a mixed methods approach. Quantitative cohort data are collected through an initial questionnaire completed by the physician during the first forensic examination and through follow-up telephone questionnaires at 6 weeks, 3 months, 6 months, and 1 year after the SA. The questionnaires measure factors associated with PTSD, mental, physical, social, and overall functional outcomes, as well as psycho-socio-judicial trajectories. Cohort participants are recruited through their forensic examination at 1 of the 5 participating centers based in France. Eligible participants are aged 15 or older, have experienced SA in the last 30 days, are fluent in French, and can be reached by phone. Qualitative data are gathered through semistructured interviews with cohort participants, individuals who have experienced SA but are not part of the cohort, and professionals involved in their psycho-socio-judicial care. RESULTS Bivariate and multivariate analyses will be conducted to examine the associations between each variable and mental, physical, social, and judicial outcomes. Predictive analyses will be performed using multiple prediction algorithms to forecast PTSD. Qualitative data will be integrated with quantitative data to identify psycho-socio-judicial trajectories and enhance the prediction of PTSD. Additionally, data on the perceptions and needs of individuals who have experienced SA will be analyzed independently to gain a deeper understanding of their experiences and requirements. CONCLUSIONS This project will collect extensive qualitative and quantitative data that have never been gathered over such an extended period, leading to unprecedented insights into the psycho-socio-judicial trajectories of individuals who have recently experienced SA. It represents the initial phase of developing a functional artificial intelligence tool that forensic practitioners can use to better guide individuals who have recently experienced SA, with the aim of preventing the onset of PTSD. Furthermore, it will contribute to addressing the existing gap in the literature regarding the accessibility and effectiveness of support services for individuals who have experienced SA in Europe. This comprehensive approach, encompassing the entire psycho-socio-judicial continuum and taking into account the viewpoints of SA survivors, will enable the generation of innovative recommendations for enhancing their care across all stages, starting from the initial forensic examination. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46652.
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Affiliation(s)
- Emma Fedele
- Institute for Interdisciplinary Research on Social Issues (UMR 8156), Aubervilliers, France
- Department of Health, Medicine and Human Biology, Sorbonne Paris Nord University (Paris 13), Bobigny, France
| | - Victor Trousset
- Department of Legal and Social Medicine, Jean Verdier Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Bondy, France
| | - Thibault Schalk
- Department of Legal and Social Medicine, Jean Verdier Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Bondy, France
| | - Juliette Oliero
- Department of Legal and Social Medicine, Jean Verdier Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Bondy, France
| | - Thomas Fovet
- Lille Neuroscience & Cognition Research Center, Regional University Hospital of Lille, University of Lille, Lille, France
| | - Thomas Lefevre
- Institute for Interdisciplinary Research on Social Issues (UMR 8156), Aubervilliers, France
- Department of Health, Medicine and Human Biology, Sorbonne Paris Nord University (Paris 13), Bobigny, France
- Department of Legal and Social Medicine, Jean Verdier Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Bondy, France
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11
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de Lacy N, Ramshaw MJ, McCauley E, Kerr KF, Kaufman J, Nathan Kutz J. Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence. Transl Psychiatry 2023; 13:314. [PMID: 37816706 PMCID: PMC10564881 DOI: 10.1038/s41398-023-02599-9] [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: 06/03/2022] [Revised: 09/10/2023] [Accepted: 09/20/2023] [Indexed: 10/12/2023] Open
Abstract
Three-quarters of lifetime mental illness occurs by the age of 24, but relatively little is known about how to robustly identify youth at risk to target intervention efforts known to improve outcomes. Barriers to knowledge have included obtaining robust predictions while simultaneously analyzing large numbers of different types of candidate predictors. In a new, large, transdiagnostic youth sample and multidomain high-dimension data, we used 160 candidate predictors encompassing neural, prenatal, developmental, physiologic, sociocultural, environmental, emotional and cognitive features and leveraged three different machine learning algorithms optimized with a novel artificial intelligence meta-learning technique to predict individual cases of anxiety, depression, attention deficit, disruptive behaviors and post-traumatic stress. Our models tested well in unseen, held-out data (AUC ≥ 0.94). By utilizing a large-scale design and advanced computational approaches, we were able to compare the relative predictive ability of neural versus psychosocial features in a principled manner and found that psychosocial features consistently outperformed neural metrics in their relative ability to deliver robust predictions of individual cases. We found that deep learning with artificial neural networks and tree-based learning with XGBoost outperformed logistic regression with ElasticNet, supporting the conceptualization of mental illnesses as multifactorial disease processes with non-linear relationships among predictors that can be robustly modeled with computational psychiatry techniques. To our knowledge, this is the first study to test the relative predictive ability of these gold-standard algorithms from different classes across multiple mental health conditions in youth within the same study design in multidomain data utilizing >100 candidate predictors. Further research is suggested to explore these findings in longitudinal data and validate results in an external dataset.
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Affiliation(s)
- Nina de Lacy
- Huntsman Mental Health Institute, Salt Lake City, UT, 84103, USA.
- Department of Psychiatry, University of Utah, Salt Lake City, UT, 84103, USA.
| | - Michael J Ramshaw
- Huntsman Mental Health Institute, Salt Lake City, UT, 84103, USA
- Department of Psychiatry, University of Utah, Salt Lake City, UT, 84103, USA
| | - Elizabeth McCauley
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - J Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
- AI Institute for Dynamical Systems, Seattle, WA, USA
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12
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Wu Y, Mao K, Dennett L, Zhang Y, Chen J. Systematic review of machine learning in PTSD studies for automated diagnosis evaluation. NPJ MENTAL HEALTH RESEARCH 2023; 2:16. [PMID: 38609504 PMCID: PMC10955977 DOI: 10.1038/s44184-023-00035-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/18/2023] [Indexed: 04/14/2024]
Abstract
Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.
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Affiliation(s)
- Yuqi Wu
- Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB, Canada
| | - Kaining Mao
- Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB, Canada
| | - Liz Dennett
- Scott Health Sciences Library, University of Alberta, Edmonton, AB, Canada
| | - Yanbo Zhang
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
| | - Jie Chen
- Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB, Canada.
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13
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Papini S, Norman SB, Campbell-Sills L, Sun X, He F, Kessler RC, Ursano RJ, Jain S, Stein MB. Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment. JAMA Netw Open 2023; 6:e2321273. [PMID: 37389870 PMCID: PMC10314304 DOI: 10.1001/jamanetworkopen.2023.21273] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/16/2023] [Indexed: 07/01/2023] Open
Abstract
Importance Military deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience. Objective To develop and validate a machine learning (ML) model to predict postdeployment PTSD. Design, Setting, and Participants This diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022. Main Outcomes and Measures Posttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse. Results This study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion. Conclusions and Relevance In this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.
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Affiliation(s)
- Santiago Papini
- Department of Psychiatry, University of California, San Diego, La Jolla
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Sonya B. Norman
- Department of Psychiatry, University of California, San Diego, La Jolla
- National Center for PTSD, White River Junction, Vermont
- Veterans Affairs Center of Excellence for Stress and Mental Health, San Diego, California
| | | | - Xiaoying Sun
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Feng He
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Robert J. Ursano
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Sonia Jain
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Murray B. Stein
- Department of Psychiatry, University of California, San Diego, La Jolla
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
- Psychiatry Service, Veterans Affairs San Diego Healthcare System, San Diego, California
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14
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Qasrawi R, Vicuna Polo S, Abu Khader R, Abu Al-Halawa D, Hallaq S, Abu Halaweh N, Abdeen Z. Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments. Front Psychiatry 2023; 14:1071622. [PMID: 37304448 PMCID: PMC10250653 DOI: 10.3389/fpsyt.2023.1071622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Mental health and cognitive development are critical aspects of a child's overall well-being; they can be particularly challenging for children living in politically violent environments. Children in conflict areas face a range of stressors, including exposure to violence, insecurity, and displacement, which can have a profound impact on their mental health and cognitive development. Methods This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. Results This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. Discussion The findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine
- Department of Computer Engineering, Istinye University, Istanbul, Türkiye
| | - Stephanny Vicuna Polo
- Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine
| | - Rami Abu Khader
- Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine
| | | | - Sameh Hallaq
- Al-Quds Bard College for Arts and Sciences, Al-Quds University, Jerusalem, Palestine
| | - Nael Abu Halaweh
- Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine
| | - Ziad Abdeen
- Faculty of Medicine, Al-Quds University, Jerusalem, Palestine
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15
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Rountree-Harrison D, Berkovsky S, Kangas M. Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review. Int J Psychophysiol 2023; 185:27-49. [PMID: 36720392 DOI: 10.1016/j.ijpsycho.2023.01.009] [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: 06/14/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research.
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Affiliation(s)
- Darius Rountree-Harrison
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia; New South Wales Service for the Rehabilitation and Treatment of Torture and Trauma Survivors (STARTTS), 152-168 The Horsley Drive Carramar, New South Wales 2163, Australia.
| | - Shlomo Berkovsky
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia
| | - Maria Kangas
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia
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16
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A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare (Basel) 2023; 11:healthcare11030285. [PMID: 36766860 PMCID: PMC9914523 DOI: 10.3390/healthcare11030285] [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: 11/10/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.
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17
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Zhang J, Sami S, Meiser-Stedman R. Acute stress and PTSD among trauma-exposed children and adolescents: Computational prediction and interpretation. J Anxiety Disord 2022; 92:102642. [PMID: 36356479 DOI: 10.1016/j.janxdis.2022.102642] [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: 01/10/2022] [Revised: 07/02/2022] [Accepted: 10/14/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND Youth receiving medical care for injury are at risk of PTSD. Therefore, accurate prediction of chronic PTSD at an early stage is needed. Machine learning (ML) offers a promising approach to precise prediction and interpretation. AIMS The study proposes a clinically useful predictive model for PTSD 6-12 months after injury, analyzing the relationship among predictors, and between predictors and outcomes. METHODS A ML approach was utilized to train models based on 1167 children and adolescents of nine perspective studies. Demographics, trauma characteristics and acute traumatic stress (ASD) symptoms were used as initial predictors. PTSD diagnosis at six months was derived using DSM-IV PTSD diagnostic criteria. Models were validated on external datasets. Shapley value and partial dependency plot (PDP) were applied to interpret the final model. RESULTS A random forest model with 13 predictors (age, ethnicity, trauma type, intrusive memories, nightmares, reliving, distress, dissociation, cognitive avoidance, sleep, irritability, hypervigilance and startle) yielded F-scores of.973,0.902 and.961 with training and two external datasets. Shapley values were calculated for individual and grouped predictors. A cumulative effect for intrusion symptoms was observed. PDP showed a non-linear relationship between age and PTSD, and between ASD symptom severity and PTSD. A 43 % difference in the risk between non-minority and minority ethnic groups was detected. CONCLUSIONS A ML model demonstrated excellent classification performance and good potential for clinical utility, using a few easily obtainable variables. Model interpretation gave a comprehensive quantitative analysis on the operations among predictors, in particular ASD symptoms.
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Affiliation(s)
- Joyce Zhang
- Department of Clinical Psychology, Norwich Medical School, University of East Anglia, UK.
| | - Saber Sami
- Dementia Research, Norwich Medical School, University of East Anglia, UK
| | - Richard Meiser-Stedman
- Department of Clinical Psychology, Norwich Medical School, University of East Anglia, UK
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18
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Pedraza LK, Sierra RO, de Oliveira Alvares L. Systems consolidation and fear memory generalisation as a potential target for trauma-related disorders. World J Biol Psychiatry 2022; 23:653-665. [PMID: 35001808 DOI: 10.1080/15622975.2022.2027010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Fear memory generalisation is a central hallmark in the broad range of anxiety and trauma-related disorders. Recent findings suggest that fear generalisation is closely related to hippocampal dependency during retrieval. In this review, we describe the current understanding about memory generalisation and its potential influence in fear attenuation through pharmacological and behavioural interventions. In light of systems consolidation framework, we propose that keeping memory precision could be a key step to enhance therapeutic outcomes.
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Affiliation(s)
- Lizeth K Pedraza
- Laboratório de Neurobiologia da Memória, Biophysics Department, Biosciences Institute, 91.501-970, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,Department of Physiology, University of Szeged, Szeged, Hungary
| | - Rodrigo O Sierra
- Laboratório de Neurobiologia da Memória, Biophysics Department, Biosciences Institute, 91.501-970, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,Department of Physiology, University of Szeged, Szeged, Hungary
| | - Lucas de Oliveira Alvares
- Laboratório de Neurobiologia da Memória, Biophysics Department, Biosciences Institute, 91.501-970, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,Graduate Program in Neuroscience, Institute of Health Sciences, Porto Alegre, Brazil
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19
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Stoitsas K, Bahulikar S, de Munter L, de Jongh MAC, Jansen MAC, Jung MM, van Wingerden M, Van Deun K. Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery. Sci Rep 2022; 12:16990. [PMID: 36216874 PMCID: PMC9550811 DOI: 10.1038/s41598-022-21390-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 09/27/2022] [Indexed: 12/29/2022] Open
Abstract
Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised techniques are employed to cluster patients based on longitudinal recovery profiles. Subsequently, these data-driven clusters were assessed on clinical validity by experts and used as targets in supervised machine learning models. We present a formalised analysis of the obtained clusters that incorporates evaluation of (i) statistical and machine learning metrics, (ii) clusters clinical validity with descriptive statistics and medical expertise. Clusters quality assessment revealed that clusters obtained through a Bayesian method (High Dimensional Supervised Classification and Clustering) and a Deep Gaussian Mixture model, in combination with oversampling and a Random Forest for supervised learning of the cluster assignments provided among the most clinically sensible partitioning of patients. Other methods that obtained higher classification accuracy suffered from cluster solutions with large majority classes or clinically less sensible classes. Models that used just physical or a mix of physical and psychological outcomes proved to be among the most sensible, suggesting that clustering on psychological outcomes alone yields recovery profiles that do not conform to known risk factors.
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Affiliation(s)
- Kostas Stoitsas
- Department of Methodology and Statistics, Tilburg University, Tilburg, 5000 LE, The Netherlands.
| | - Saurabh Bahulikar
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, 5000 LE, The Netherlands
| | - Leonie de Munter
- Department Traumatology, ETZ Hospital, Hilvarenbeekseweg 60, 5022 GC, Tilburg, The Netherlands
| | - Mariska A C de Jongh
- Network Emergency Care Brabant, Brabant Trauma Registry, Hilvarenbeekseweg 60, 5022 GC, Tilburg, The Netherlands
| | - Maria A C Jansen
- Network Emergency Care Brabant, Brabant Trauma Registry, Hilvarenbeekseweg 60, 5022 GC, Tilburg, The Netherlands
| | - Merel M Jung
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, 5000 LE, The Netherlands
| | - Marijn van Wingerden
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, 5000 LE, The Netherlands
| | - Katrijn Van Deun
- Department of Methodology and Statistics, Tilburg University, Tilburg, 5000 LE, The Netherlands
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20
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Marengo D, Hoeboer CM, Veldkamp BP, Olff M. Text mining to improve screening for trauma-related symptoms in a global sample. Psychiatry Res 2022; 316:114753. [PMID: 35940089 DOI: 10.1016/j.psychres.2022.114753] [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: 02/02/2022] [Revised: 07/22/2022] [Accepted: 07/27/2022] [Indexed: 11/16/2022]
Abstract
Previous studies showed that textual information could be used to screen respondents for posttraumatic stress disorder (PTSD). In this study, we explored the feasibility of using language features extracted from short text descriptions respondents provided of stressful events to predict trauma-related symptoms assessed using the Global Psychotrauma Screen. Texts were analyzed with both closed- and open-vocabulary methods to extract language features representing the occurrence of words, phrases, or specific topics in the description of stressful events. We also evaluated whether combining language features with self-report information, including respondents' demographics, event characteristics, and risk factors for trauma-related disorders, would improve the prediction performance. Data were collected using an online survey on a cross-national sample of 5048 respondents. Results showed that language data achieved the highest predictive power when both closed- and open-vocabulary features were included as predictors. Combining language data and self-report information resulted in a significant increase in performance and in a model which achieved good accuracy as a screener for probable PTSD diagnosis (.7 < AUC ≤ .8), with similar results regardless of the length of the text description of the event. Overall, results indicated that short texts add to the detection of trauma-related symptoms and probable PTSD diagnosis.
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Affiliation(s)
- D Marengo
- Department of Psychology, University of Turin, Via Verdi 10, Turin 10124, Italy
| | - C M Hoeboer
- Department of Psychiatry, Amsterdam Public Health, University of Amsterdam, Amsterdam UMC location, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands.
| | - B P Veldkamp
- ARQ National Psychotrauma Centre, Diemen, the Netherlands
| | | | - M Olff
- Department of Psychiatry, Amsterdam Public Health, University of Amsterdam, Amsterdam UMC location, Meibergdreef 9, Amsterdam 1105 AZ, the Netherlands; Department of Learning, Data-Analytics, and Technology, Faculty of Behavioral Management and Social Sciences, University of Twente, the Netherlands
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21
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Howe ES, Fisher AJ. Identifying and predicting posttraumatic stress symptom states in adults with posttraumatic stress disorder. J Trauma Stress 2022; 35:1508-1520. [PMID: 35864591 DOI: 10.1002/jts.22857] [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: 09/17/2021] [Revised: 04/20/2022] [Accepted: 04/20/2022] [Indexed: 11/06/2022]
Abstract
Between-person heterogeneity of posttraumatic stress disorder (PTSD) is well established. Within-person analyses and the DSM-5 suggest that heterogeneity may also be evident within individuals across time as they move through social contexts and biological cycles. Modeling within-person symptom-level fluctuations may confirm such heterogeneity, elucidate mechanisms of disorder maintenance, and inform time- and person-specific interventions. The present study aimed to identify and predict discrete within-person disorder presentations, or symptom states, and explore group-level patterns of these states. Adults (N = 20, 60.0% male, M age = 38.25 years) with PTSD responded to symptom surveys four times per day for 30 days. We subjected each individual's dataset to Gaussian finite mixture modeling (GFMM) to uncover latent, within-person classes of symptom levels (i.e., states) and predicted those states with idiographic elastic net regularized regression using a set of time-based and behavioral predictors. Next, we conducted a GFMM of the within-person GFMM outputs and tested idiographic prediction models of these states. Multiple within-person states were revealed for 19 of 20 participants (Mdn = 4; 66 for the full sample). Prediction models were moderately successful, M AUC = .66 (d = 0.58), range: .50-1.00. The GFMM of the within-person model outputs revealed two states: one with above-average and one with below-average symptom levels. Prediction models were, again, moderately successful, M AUC = .66; range: .50-.89. The findings provide evidence for within-person heterogeneity of PTSD as well as between-person similarities and suggest that future work should incorporate additional contextual variables as symptom state predictors.
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Affiliation(s)
- Esther S Howe
- Department of Psychology, University of California, Berkeley, Berkeley, California, USA
| | - Aaron J Fisher
- Department of Psychology, University of California, Berkeley, Berkeley, California, USA
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22
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The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review. NPJ Digit Med 2022; 5:87. [PMID: 35798934 PMCID: PMC9262920 DOI: 10.1038/s41746-022-00631-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/08/2022] [Indexed: 11/08/2022] Open
Abstract
Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer's disease (n = 7), mild cognitive impairment (n = 6), schizophrenia (n = 3), bipolar disease (n = 2), autism spectrum disorder (n = 1), obsessive-compulsive disorder (n = 1), post-traumatic stress disorder (n = 1), and psychotic disorders (n = 1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. Healthcare professionals in the field should cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.
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23
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Identifying posttraumatic stress disorder staging from clinical and sociodemographic features: a proof-of-concept study using a machine learning approach. Psychiatry Res 2022; 311:114489. [PMID: 35276574 DOI: 10.1016/j.psychres.2022.114489] [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: 01/12/2022] [Revised: 02/16/2022] [Accepted: 02/26/2022] [Indexed: 11/23/2022]
Abstract
This proof-of-concept study aimed to investigate the viability of a predictive model to support posttraumatic stress disorder (PTSD) staging. We performed a naturalistic, cross-sectional study at two Brazilian centers: the Psychological Trauma Research and Treatment (NET-Trauma) Program at Universidade Federal of Rio Grande do Sul, and the Program for Research and Care on Violence and PTSD (PROVE), at Universidade Federal of São Paulo. Five supervised machine-learning algorithms were tested: Elastic Net, Gradient Boosting Machine, Random Forest, Support Vector Machine, and C5.0, using clinical (Clinician-Administered PTSD Scale version 5) and sociodemographic features. A hundred and twelve patients were enrolled (61 from NET-Trauma and 51 from PROVE). We found a model with four classes suitable for the PTSD staging, with best performance metrics using the C5.0 algorithm to CAPS-5 15-items plus sociodemographic features, with an accuracy of 65.6% for the train dataset and 52.9% for the test dataset (both significant). The number of symptoms, CAPS-5 total score, global severity score, and presence of current/previous trauma events appear as main features to predict PTSD staging. This is the first study to evaluate staging in PTSD with machine learning algorithms using accessible clinical and sociodemographic features, which may be used in future research.
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24
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Fitzgerald JM, Webb EK, Weis CN, Huggins AA, Bennett KP, Miskovich TA, Krukowski JL, deRoon-Cassini TA, Larson CL. Hippocampal Resting-State Functional Connectivity Forecasts Individual Posttraumatic Stress Disorder Symptoms: A Data-Driven Approach. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:139-149. [PMID: 34478884 PMCID: PMC8825698 DOI: 10.1016/j.bpsc.2021.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/18/2021] [Accepted: 08/22/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is a debilitating disorder, and there is no current accurate prediction of who develops it after trauma. Neurobiologically, individuals with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior cingulate), and these aberrations correlate with severity of illness. Previous small-scale research (n < 25) has also shown that hippocampal rsFC measured acutely after trauma is predictive of future severity using a region-of-interest-based approach. While this is a promising biomarker, to date, no study has used a data-driven approach to test whole-brain hippocampal FC patterns in forecasting the development of PTSD symptoms. METHODS A total of 98 adults at risk of PTSD were recruited from the emergency department after traumatic injury and completed resting-state functional magnetic resonance imaging (8 min) within 1 month; 6 months later, they completed the Clinician-Administered PTSD Scale for DSM-5 for assessment of PTSD symptom severity. Whole-brain rsFC values with bilateral hippocampi were extracted (using CONN) and used in a machine learning kernel ridge regression analysis (PRoNTo); a k-folds (k = 10) and 70/30 testing versus training split approach were used for cross-validation (1000 iterations to bootstrap confidence intervals for significance values). RESULTS Acute hippocampal rsFC significantly predicted Clinician-Administered PTSD Scale for DSM-5 scores at 6 months (r = 0.30, p = .006; mean squared error = 120.58, p = .006; R2 = 0.09, p = .025). In post hoc analyses, hippocampal rsFC remained significant after controlling for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity at 6 months (B = 0.59, SE = 0.20, p = .003). CONCLUSIONS Findings suggest that functional connectivity of the hippocampus across the brain acutely after traumatic injury is associated with prospective PTSD symptom severity.
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Affiliation(s)
| | - Elisabeth Kate Webb
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
| | - Carissa N. Weis
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
| | - Ashley A. Huggins
- Medical University of South Carolina, Department of Psychiatry, Charleston, SC, USA
| | | | | | | | - Terri A. deRoon-Cassini
- Medical College of Wisconsin, Department of Surgery, Division of Trauma & Acute Care Surgery, Milwaukee, WI, USA
| | - Christine L. Larson
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
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25
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Sawalha J, Yousefnezhad M, Shah Z, Brown MRG, Greenshaw AJ, Greiner R. Detecting Presence of PTSD Using Sentiment Analysis From Text Data. Front Psychiatry 2022; 12:811392. [PMID: 35178000 PMCID: PMC8844448 DOI: 10.3389/fpsyt.2021.811392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/27/2021] [Indexed: 12/15/2022] Open
Abstract
Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.
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Affiliation(s)
- Jeff Sawalha
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
- Department of Computer Science, University of Alberta, Edmonton, AB, Canada
- Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
| | - Muhammad Yousefnezhad
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
- Department of Computer Science, University of Alberta, Edmonton, AB, Canada
- Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
| | - Zehra Shah
- Department of Computer Science, University of Alberta, Edmonton, AB, Canada
- Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
| | - Matthew R. G. Brown
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
- Department of Computer Science, University of Alberta, Edmonton, AB, Canada
| | | | - Russell Greiner
- Department of Computer Science, University of Alberta, Edmonton, AB, Canada
- Department of Computing Science, Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada
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26
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Rousseau S, Polachek IS, Frenkel TI. A machine learning approach to identifying pregnant women's risk for persistent post-traumatic stress following childbirth. J Affect Disord 2022; 296:136-149. [PMID: 34601301 DOI: 10.1016/j.jad.2021.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/11/2021] [Accepted: 09/12/2021] [Indexed: 10/20/2022]
Abstract
INTRO Recent literature identifies childbirth as a potentially traumatic event, following which mothers may develop symptoms of Post-Traumatic-Stress-Following-Childbirth (PTS-FC). Especially when persistent, PTS-FC may interfere with mothers' caregiving and associated infant development, underscoring the need for accurate predictive screening of risk. Drawing on recent developments in advanced statistical modeling, the aim of the current study was to identify a set of prenatal indicators and prediction rules that may accurately identify pregnant women's risk for developing symptoms of PTS-FC which persist throughout the early postpartum period. METHODS 182 women from the general population completed a comprehensive set of approximately 200 potentially predictive questions during pregnancy, and subsequently reported on their acute stress and PTS-FC at three days, one month, and three months postpartum (self-report and clinician-administered interview). Based on the postpartum acute stress and PTS-FC data, women were classified into profiles of "Stable-High-PTS-FC" and "Stable-Low-PTS-FC" by means of Latent-Class Analyses. Prenatal data were modeled to identify women at risk for "Stable-High PTS-FC". RESULTS Employing machine-learning decision-tree analyses, a total of 36 questions and 7 prediction-rules were selected. Based on a cost-rate of 15 versus 100 for false-negative "Stable-Low-PTS-FC" versus false-negative "Stable-High-PTS-FC", the final model showed 80.6% accuracy for "Stable-High-PTS-FC" prediction. DISCUSSION This study identifies a short set of questions and prediction rules that may be included in future large-scale validation studies aimed at developing and validating a brief PTS-FC screening instrument that could be implemented in general population prenatal healthcare practice. Accurate screening would allow for selective administering of preventive interventions towards women at risk.
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Affiliation(s)
- Sofie Rousseau
- Ziama Arkin Infancy Institute, Interdisciplinary Center (IDC) Herzliya, Hanadiv 71, 1st floor, Herzliya 46485, Israel; Baruch Ivcher School of Psychology, Interdisciplinary Center (IDC) Herzliya, HaUniversity 8, Herzliya 4610101, Israel
| | - Inbal Shlomi Polachek
- Be'er Ya'akov Medical Center, Israel; Tel Aviv University, Sackler School of Medicine, Tel Aviv, Israel
| | - Tahl I Frenkel
- Ziama Arkin Infancy Institute, Interdisciplinary Center (IDC) Herzliya, Hanadiv 71, 1st floor, Herzliya 46485, Israel; Baruch Ivcher School of Psychology, Interdisciplinary Center (IDC) Herzliya, HaUniversity 8, Herzliya 4610101, Israel.
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27
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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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28
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Oltmanns JR, Schwartz HA, Ruggero C, Son Y, Miao J, Waszczuk M, Clouston SAP, Bromet EJ, Luft BJ, Kotov R. Artificial intelligence language predictors of two-year trauma-related outcomes. J Psychiatr Res 2021; 143:239-245. [PMID: 34509091 PMCID: PMC8935804 DOI: 10.1016/j.jpsychires.2021.09.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/29/2021] [Accepted: 09/01/2021] [Indexed: 11/19/2022]
Abstract
BACKGROUND Recent research on artificial intelligence has demonstrated that natural language can be used to provide valid indicators of psychopathology. The present study examined artificial intelligence-based language predictors (ALPs) of seven trauma-related mental and physical health outcomes in responders to the World Trade Center disaster. METHODS The responders (N = 174, Mage = 55.4 years) provided daily voicemail updates over 14 days. Algorithms developed using machine learning in large social media discovery samples were applied to the voicemail transcriptions to derive ALP scores for several risk factors (depressivity, anxiousness, anger proneness, stress, and personality). Responders also completed self-report assessments of these risk factors at baseline and trauma-related mental and physical health outcomes at two-year follow-up (including symptoms of depression, posttraumatic stress disorder, sleep disturbance, respiratory problems, and GERD). RESULTS Voicemail ALPs were significantly associated with a majority of the trauma-related outcomes at two-year follow-up, over and above corresponding baseline self-reports. ALPs showed significant convergence with corresponding self-report scales, but also considerable uniqueness from each other and from self-report scales. LIMITATIONS The study has a relatively short follow-up period relative to trauma occurrence and a limited sample size. CONCLUSIONS This study shows evidence that ALPs may provide a novel, objective, and clinically useful approach to forecasting, and may in the future help to identify individuals at risk for negative health outcomes.
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29
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Schultebraucks K, Qian M, Abu-Amara D, Dean K, Laska E, Siegel C, Gautam A, Guffanti G, Hammamieh R, Misganaw B, Mellon SH, Wolkowitz OM, Blessing EM, Etkin A, Ressler KJ, Doyle FJ, Jett M, Marmar CR. Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors. Mol Psychiatry 2021; 26:5011-5022. [PMID: 32488126 PMCID: PMC8589682 DOI: 10.1038/s41380-020-0789-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 05/12/2020] [Accepted: 05/15/2020] [Indexed: 12/22/2022]
Abstract
Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.
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Affiliation(s)
- Katharina Schultebraucks
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA.
- Department of Emergency Medicine, Vagelos School of Physicians and Surgeons, Columbia University Medical Center, New York, NY, USA.
- Data Science Institute, Columbia University, New York, NY, USA.
| | - Meng Qian
- Department of Psychiatry, Center for Alcohol Use Disorder and PTSD, New York University Grossman School of Medicine, New York, NY, USA
| | - Duna Abu-Amara
- Department of Psychiatry, Center for Alcohol Use Disorder and PTSD, New York University Grossman School of Medicine, New York, NY, USA
| | - Kelsey Dean
- Harvard Paulson School of Engineering & Applied Sciences, Boston, MA, USA
| | - Eugene Laska
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Department of Population Health, Biostatistics Division, New York University Grossman School of Medicine, New York, NY, USA
| | - Carole Siegel
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
- Department of Population Health, Biostatistics Division, New York University Grossman School of Medicine, New York, NY, USA
| | - Aarti Gautam
- Integrative Systems Biology, US Army Center for Environmental Health Research, USACEHR, Fort Detrick, Frederick, MD, USA
| | - Guia Guffanti
- McLean Hospital, Harvard University, Boston, MA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Rasha Hammamieh
- Integrative Systems Biology, US Army Center for Environmental Health Research, USACEHR, Fort Detrick, Frederick, MD, USA
| | - Burook Misganaw
- Harvard Paulson School of Engineering & Applied Sciences, Boston, MA, USA
| | - Synthia H Mellon
- Department of Obstetrics, Gynecology & Reproductive Sciences, University of California, San Francisco, CA, USA
| | - Owen M Wolkowitz
- Department of Psychiatry/Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Esther M Blessing
- Department of Psychiatry, Center for Alcohol Use Disorder and PTSD, New York University Grossman School of Medicine, New York, NY, USA
| | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Kerry J Ressler
- McLean Hospital, Harvard University, Boston, MA, USA
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Francis J Doyle
- Harvard Paulson School of Engineering & Applied Sciences, Boston, MA, USA
| | - Marti Jett
- Integrative Systems Biology, US Army Center for Environmental Health Research, USACEHR, Fort Detrick, Frederick, MD, USA
| | - Charles R Marmar
- Department of Psychiatry, Center for Alcohol Use Disorder and PTSD, New York University Grossman School of Medicine, New York, NY, USA
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Roth CB, Papassotiropoulos A, Brühl AB, Lang UE, Huber CG. Psychiatry in the Digital Age: A Blessing or a Curse? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:8302. [PMID: 34444055 PMCID: PMC8391902 DOI: 10.3390/ijerph18168302] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022]
Abstract
Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor-patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.
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Affiliation(s)
- Carl B. Roth
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Andreas Papassotiropoulos
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
- Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Birmannsgasse 8, CH-4055 Basel, Switzerland
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, Birmannsgasse 8, CH-4055 Basel, Switzerland
- Biozentrum, Life Sciences Training Facility, University of Basel, Klingelbergstrasse 50/70, CH-4056 Basel, Switzerland
| | - Annette B. Brühl
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Undine E. Lang
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
| | - Christian G. Huber
- University Psychiatric Clinics Basel, Clinic for Adults, University of Basel, Wilhelm Klein-Strasse 27, CH-4002 Basel, Switzerland; (A.P.); (A.B.B.); (U.E.L.); (C.G.H.)
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Obuobi-Donkor G, Eboreime E, Bond J, Phung N, Eyben S, Hayward J, Zhang Y, MacMaster F, Clelland S, Greiner R, Jones C, Cao B, Brémault-Phillips S, Wells K, Li XM, Hilario C, Greenshaw AJ, Agyapong VIO. An E-Mental Health Solution to Prevent and Manage Post-Traumatic Stress Injuries among First Responders in Alberta: Protocol for the Implementation and Evaluation of Text4PTSI and Text4Wellbeing (Preprint). JMIR Res Protoc 2021; 11:e30680. [PMID: 35468094 PMCID: PMC9086885 DOI: 10.2196/30680] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 02/07/2022] [Accepted: 02/22/2022] [Indexed: 11/17/2022] Open
Abstract
Background First responders are confronted with traumatic events in their work that has a substantial toll on their psychological health and may contribute to or result in posttraumatic stress injuries (PTSIs) for many responders. Persons with a PTSI usually seek management therapies. Evidence indicates that digital delivery of these therapies is an innovative, efficient, and effective way to improve PTSI symptoms as an adjunct to in-person delivery. Objective This project aims to implement and provide accessible, convenient, and economical SMS text messaging services, known as Text4PTSI and Text4Wellbeing, to first responders in Alberta, Canada; to prevent and improve the symptoms of PTSI among first responders; and to improve their overall quality of life. We will evaluate posttraumatic symptoms and the impact of Text4PTSI and Text4Wellbeing on stress, anxiety, and depression in relation to the correspondents’ demographic backgrounds. Methods First responders who subscribe to Text4PTSI or Text4Wellbeing receive daily supportive and psychoeducational SMS text messages for 6 months. The SMS text messages are preprogrammed into an online software program that delivers messages to subscribers. Baseline and follow-up data are collected through online questionnaires using validated scales at enrollment, 6 weeks, 12 weeks, and 24 weeks (end point). In-depth interviews will be conducted to assess satisfaction with the text-based intervention. Results We hypothesize that participants who enroll in this program will have improved PTSI symptoms; increased or improved quality of life; and significant reduction in associated stress, depression, and anxiety symptoms, among other psychological concerns. Improvement will be determined in comparison to established baseline parameters. Conclusions This research will be beneficial for practitioners and will inform policy-making and decision-making regarding psychological interventions for PTSI. Lessons from this study will inform the scale-up of the intervention, a cost-effective, zero contact therapeutic option to manage PTSI. International Registered Report Identifier (IRRID) PRR1-10.2196/30680
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Affiliation(s)
- Gloria Obuobi-Donkor
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Ejemai Eboreime
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Jennifer Bond
- Operational Stress Injury Clinic, Alberta Health Services, Edmonton, AB, Canada
| | - Natalie Phung
- Operational Stress Injury Clinic, Alberta Health Services, Edmonton, AB, Canada
| | - Scarlett Eyben
- Operational Stress Injury Clinic, Alberta Health Services, Edmonton, AB, Canada
| | - Jake Hayward
- Department of Emergency Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Yanbo Zhang
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Frank MacMaster
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Addiction and Mental Health, Alberta Health Services, Edmonton, AB, Canada
| | - Steven Clelland
- Addiction and Mental Health, Alberta Health Services, Edmonton, AB, Canada
| | - Russell Greiner
- Department of Computer Science, Faculty of Science, University of Alberta, Edmonton, AB, Canada
| | - Chelsea Jones
- Department of Occupational Therapy, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Suzette Brémault-Phillips
- Department of Occupational Therapy, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
- Department of Child and Youth Care, Faculty of Health and Community Studies, MacEwan University, Edmonton, AB, Canada
| | - Kristopher Wells
- Department of Child and Youth Care, Faculty of Health and Community Studies, MacEwan University, Edmonton, AB, Canada
| | - Xin-Min Li
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Carla Hilario
- Faculty of Nursing, University of Alberta, Edmonton, AB, Canada
| | - Andrew J Greenshaw
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Vincent Israel Opoku Agyapong
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
- Department of Psychiatry, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
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Abd-alrazaq A, Schneider J, Alhuwail D, Toro CT, Ahmed A, Alajlani M, Househ M. The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review (Preprint).. [DOI: 10.2196/preprints.29235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
Diagnosing mental disorders is usually not an easy task and requires a large amount of time and effort given the complex nature of mental disorders. Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders.
OBJECTIVE
This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders.
METHODS
To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. Specifically, results of the included reviews were grouped based on the target mental disorders that the AI classifiers distinguish.
RESULTS
We included 15 systematic reviews of 852 citations identified by searching all databases. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n=7), mild cognitive impairment (n=6), schizophrenia (n=3), bipolar disease (n=2), autism spectrum disorder (n=1), obsessive-compulsive disorder (n=1), post-traumatic stress disorder (n=1), and psychotic disorders (n=1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%.
CONCLUSIONS
AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. To expedite progress towards these technologies being incorporated into routine practice, we recommend that healthcare professionals in the field cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.
CLINICALTRIAL
CRD42021231558
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A systematic review of machine learning in logistics and supply chain management: current trends and future directions. BENCHMARKING-AN INTERNATIONAL JOURNAL 2021. [DOI: 10.1108/bij-10-2020-0514] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PurposeThis paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.Design/methodology/approachA systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.FindingsOver the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.Research limitations/implicationsThis review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.Originality/valueThis paper provides a systematic insight into research trends in ML in both logistics and the supply chain.
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Physiological parameters of mental health predict the emergence of post-traumatic stress symptoms in physicians treating COVID-19 patients. Transl Psychiatry 2021; 11:169. [PMID: 33723233 PMCID: PMC7957277 DOI: 10.1038/s41398-021-01299-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 02/18/2021] [Accepted: 03/02/2021] [Indexed: 12/15/2022] Open
Abstract
Lack of established knowledge and treatment strategies, and change in work environment, may altogether critically affect the mental health and functioning of physicians treating COVID-19 patients. Thus, we examined whether treating COVID-19 patients affect the physicians' mental health differently compared with physicians treating non-COVID-19 patients. In this cohort study, an association was blindly computed between physiologically measured anxiety and attention vigilance (collected from 1 May 2014 to 31 May 31 2016) and self-reports of anxiety, mental health aspects, and sleep quality (collected from 20 April to 30 June 2020, and analyzed from 1 July to 1 September 2020), of 91 physicians treating COVID-19 or non-COVID-19 patients. As a priori hypothesized, physicians treating COVID-19 patients showed a relative elevation in both physiological measures of anxiety (95% CI: 2317.69-2453.44 versus 1982.32-2068.46; P < 0.001) and attention vigilance (95% CI: 29.85-34.97 versus 22.84-26.61; P < 0.001), compared with their colleagues treating non-COVID-19 patients. At least 3 months into the pandemic, physicians treating COVID-19 patients reported high anxiety and low quality of sleep. Machine learning showed clustering to the COVID-19 and non-COVID-19 subgroups with a high correlation mainly between physiological and self-reported anxiety, and between physiologically measured anxiety and sleep duration. To conclude, the pattern of attention vigilance, heightened anxiety, and reduced sleep quality findings point the need for mental intervention aimed at those physicians susceptible to develop post-traumatic stress symptoms, owing to the consequences of fighting at the forefront of the COVID-19 pandemic.
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Stuke H, Schoofs N, Johanssen H, Bermpohl F, Ülsmann D, Schulte-Herbrüggen O, Priebe K. Predicting outcome of daycare cognitive behavioural therapy in a naturalistic sample of patients with PTSD: a machine learning approach. Eur J Psychotraumatol 2021; 12:1958471. [PMID: 34589175 PMCID: PMC8475102 DOI: 10.1080/20008198.2021.1958471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Identifying predictors for treatment outcome in patients with posttraumatic stress disorder (PTSD) is important in order to provide an effective treatment, but robust and replicated treatment outcome predictors are not available up to now. OBJECTIVES We investigated predictors of treatment outcome in a naturalistic sample of patients with PTSD admitted to an 8-week daycare cognitive behavioural therapy programme following a wide range of traumatic events. METHOD We used machine learning (linear and non-linear regressors and cross-validation) to predict outcome at discharge for 116 patients and sustained treatment effects 6 months after discharge for 52 patients who had a follow-up assessment. Predictions were based on a wide selection of demographic and clinical assessments including age, gender, comorbid psychiatric disorders, trauma history, posttraumatic symptoms, posttraumatic cognitions, depressive symptoms, general psychopathology and psychosocial functioning. RESULTS We found that demographic and clinical variables significantly, but only modestly predicted PTSD treatment outcome at discharge (r = 0.21, p = .021 for the best model) and follow-up (r = 0.31, p = .026). Among the included variables, more severe posttraumatic cognitions were negatively associated with treatment outcome. Early response in PTSD symptomatology (percentage change of symptom scores after 4 weeks of treatment) allowed more accurate predictions of outcome at discharge (r = 0.56, p < .001) and follow-up (r = 0.43, p = .001). CONCLUSION Our results underscore the importance of early treatment response for short- and long-term treatment success. Nevertheless, it remains an unresolved challenge to identify variables that can robustly predict outcome before the initiation of treatment.
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Affiliation(s)
- Heiner Stuke
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nikola Schoofs
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Helen Johanssen
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Dominik Ülsmann
- Friedrich Von Bodelschwingh-Clinic for Psychiatry, Psychotherapy and Psychosomatics, Berlin, Germany
| | - Olaf Schulte-Herbrüggen
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Friedrich Von Bodelschwingh-Clinic for Psychiatry, Psychotherapy and Psychosomatics, Berlin, Germany
| | - Kathlen Priebe
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Souza Filho EMD, Veiga Rey HC, Frajtag RM, Arrowsmith Cook DM, Dalbonio de Carvalho LN, Pinho Ribeiro AL, Amaral J. Can machine learning be useful as a screening tool for depression in primary care? J Psychiatr Res 2021; 132:1-6. [PMID: 33035759 DOI: 10.1016/j.jpsychires.2020.09.025] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 08/07/2020] [Accepted: 09/25/2020] [Indexed: 12/20/2022]
Abstract
Depression is a widespread disease with a high economic burden and a complex pathophysiology disease that is still not wholly clarified, not to mention it usually is associated as a risk factor for absenteeism at work and suicide. Just 50% of patients with depression are diagnosed in primary care, and only 15% receive treatment. Stigmatization, the coexistence of somatic symptoms, and the need to remember signs in the past two weeks can contribute to explaining this situation. In this context, tools that can serve as diagnostic screening are of great value, as they can reduce the number of undiagnosed patients. Besides, Artificial Intelligence (AI) has enabled several fruitful applications in medicine, particularly in psychiatry. This study aims to evaluate the performance of Machine Learning (ML) algorithms in the detection of depressive patients from the clinical, laboratory, and sociodemographic data obtained from the Brazilian National Network for Research on Cardiovascular Diseases from June 2016 to July 2018. The results obtained are promising. In one of them, Random Forests, the accuracy, sensibility, and area under the receiver operating characteristic curve were, respectively, 0.89, 0.90, and 0.87.
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Affiliation(s)
- Erito Marques de Souza Filho
- Universidade Federal Rural do Rio de Janeiro, Rio de Janeiro, Brazil; Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil.
| | | | | | | | | | | | - Jorge Amaral
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
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Jones C, Smith-MacDonald L, Miguel-Cruz A, Pike A, van Gelderen M, Lentz L, Shiu MY, Tang E, Sawalha J, Greenshaw A, Rhind SG, Fang X, Norbash A, Jetly R, Vermetten E, Brémault-Phillips S. Virtual Reality-Based Treatment for Military Members and Veterans With Combat-Related Posttraumatic Stress Disorder: Protocol for a Multimodular Motion-Assisted Memory Desensitization and Reconsolidation Randomized Controlled Trial. JMIR Res Protoc 2020; 9:e20620. [PMID: 33118957 PMCID: PMC7661230 DOI: 10.2196/20620] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 12/12/2022] Open
Abstract
Background Military members are at elevated risk of operational stress injuries, including posttraumatic stress disorder (PTSD) and moral injury. Although psychotherapy can reduce symptoms, some military members may experience treatment-resistant PTSD. Multimodular motion-assisted memory desensitization and reconsolidation (3MDR) has been introduced as a virtual reality (VR) intervention for military members with PTSD related to military service. The 3MDR intervention incorporates exposure therapy, psychotherapy, eye movement desensitization and reconsolidation, VR, supportive counselling, and treadmill walking. Objective The objective of this study is to investigate whether 3MDR reduces PTSD symptoms among military members with combat-related treatment-resistant PTSD (TR-PTSD); examine the technology acceptance and usability of the Computer Assisted Rehabilitation ENvironment (CAREN) and 3MDR interventions by Canadian Armed Forces service members (CAF-SMs), veterans, 3MDR clinicians, and operators; and evaluate the impact on clinicians and operators of delivering 3MDR. Methods This is a mixed-methods waitlist controlled crossover design randomized controlled trial. Participants include both CAF-SMs and veterans (N=40) aged 18-60 years with combat-related TR-PTSD (unsuccessful experience of at least 2 evidence-based trauma treatments). Participants will also include clinicians and operators (N=12) who have been trained in 3MDR and subsequently utilized this intervention with patients. CAF-SMs and veterans will receive 6 weekly 90-minute 3MDR sessions. Quantitative and qualitative data will be collected at baseline and at 1, 3, and 6 months postintervention. Quantitative data collection will include multiomic biomarkers (ie, blood and salivary proteomic and genomic profiles of neuroendocrine, immune-inflammatory mediators, and microRNA), eye tracking, electroencephalography, and physiological data. Data from outcome measures will capture self-reported symptoms of PTSD, moral injury, resilience, and technology acceptance and usability. Qualitative data will be collected from audiovisual recordings of 3MDR sessions and semistructured interviews. Data analysis will include univariate and multivariate approaches, and thematic analysis of treatment sessions and interviews. Machine learning analysis will be included to develop models for the prediction of diagnosis, symptom severity, and treatment outcomes. Results This study commenced in April 2019 and is planned to conclude in April 2021. Study results will guide the further evolution and utilization of 3MDR for military members with TR-PTSD and will have utility in treating other trauma-affected populations. Conclusions The goal of this study is to utilize qualitative and quantitative primary and secondary outcomes to provide evidence for the effectiveness and feasibility of 3MDR for treating CAF-SMs and veterans with combat-related TR-PTSD. The results will inform a full-scale clinical trial and stimulate development and adaptation of the protocol to mobile VR apps in supervised clinical settings. This study will add to knowledge of the clinical effectiveness of 3MDR, and provide the first comprehensive analysis of biomarkers, technology acceptance and usability, moral injury, resilience, and the experience of clinicians and operators delivering 3MDR. Trial Registration ISRCTN Registry 11264368; http://www.isrctn.com/ISRCTN11264368. International Registered Report Identifier (IRRID) DERR1-10.2196/20620
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Affiliation(s)
- Chelsea Jones
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Lorraine Smith-MacDonald
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Antonio Miguel-Cruz
- Department of Occupational Therapy, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada.,Glenrose Rehabilitation Hospital Research Innovation and Technology (GRRIT), Glenrose Rehabilitation Hospital, Edmonton, AB, Canada
| | - Ashley Pike
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Marieke van Gelderen
- ARQ Centrum'45, Diemen, Netherlands.,Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands
| | - Liana Lentz
- School of Health Studies, Western University, London, ON, Canada
| | - Maria Y Shiu
- Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, Canada
| | - Emily Tang
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Jeffrey Sawalha
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Andrew Greenshaw
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Shawn G Rhind
- Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, Canada
| | - Xin Fang
- Department of Obstetrics and Gynecology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Adrian Norbash
- Canadian Forces Health Services, Department of National Defense, Edmonton, AB, Canada
| | - Rakesh Jetly
- Department of Mental Health, Canadian Forces Health Services, Department of National Defense, Ottawa, ON, Canada
| | - Eric Vermetten
- Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands.,Military Mental Health Research, Ministry of Defense, Utrecht, Netherlands.,ARQ National Psychotrauma Centre, Deimen, Netherlands
| | - Suzette Brémault-Phillips
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada.,Department of Occupational Therapy, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
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Lin E, Lin CH, Lane HY. Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches. Int J Mol Sci 2020; 21:ijms21030969. [PMID: 32024055 PMCID: PMC7037937 DOI: 10.3390/ijms21030969] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 01/25/2020] [Accepted: 01/30/2020] [Indexed: 12/22/2022] Open
Abstract
A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
- Correspondence: (C.-H.L.); (H.-Y.L.)
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40402, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40402, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
- Correspondence: (C.-H.L.); (H.-Y.L.)
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O’Leary B, Shih CH, Chen T, Xie H, Cotton AS, Xu KS, Morey R, Wang X. Classification of PTSD and Non-PTSD Using Cortical Structural Measures in Machine Learning Analyses—Preliminary Study of ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup. Brain Inform 2020. [DOI: 10.1007/978-3-030-59277-6_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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