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Journal of Mental Health Volume 28, 2019 - Issue 1
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Original Articles

Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort

Daniel LeightleyKing’s Centre for Military Health Research, Institute of Psychiatry, Psychology & Neuroscience, King’s College, London, UK; Correspondence[email protected]
ORCID Iconhttp://orcid.org/0000-0001-9512-752X
ORCID Icon,
Victoria WilliamsonKing’s Centre for Military Health Research, Institute of Psychiatry, Psychology & Neuroscience, King’s College, London, UK; ORCID Iconhttp://orcid.org/0000-0002-3110-9856
ORCID Icon,
John DarbySchool of Computing, Mathematics and Digital Technology, Manchester Metropolitan University;
&
Nicola T. FearKing’s Centre for Military Health Research, Institute of Psychiatry, Psychology & Neuroscience, King’s College, London, UK; ;Academic Department of Military Mental Health, Institute of Psychiatry, Psychology & Neuroscience, King’s College, London, UKORCID Iconhttp://orcid.org/0000-0002-5792-2925
ORCID Icon
Pages 34-41 | Received 26 Oct 2017, Accepted 21 Aug 2018, Published online: 16 Nov 2018

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Karen-Inge Karstoft, Kasper Eskelund, Jaimie L. Gradus, Søren B. Andersen & Lars R. Nissen. (2023) Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models. Journal of Psychiatric Research 163, pages 109-117.
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Nikolaos Papadakis, K Havenetidis, D Papadopoulos & A Bissas. (2023) Employing body-fixed sensors and machine learning to predict physical activity in military personnel. BMJ Military Health 169:2, pages 152-156.
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Darius Rountree-Harrison, Shlomo Berkovsky & Maria Kangas. (2023) Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review. International Journal of Psychophysiology 185, pages 27-49.
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Daniel Leightley & D Murphy. (2023) Personalised digital technology for mental health in the armed forces: the potential, the hype and the dangers. BMJ Military Health 169:1, pages 81-83.
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Hasan Zafari, Leanne Kosowan, Farhana Zulkernine & Alexander Signer. (2021) Diagnosing post-traumatic stress disorder using electronic medical record data. Health Informatics Journal 27:4, pages 146045822110532.
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Katharina Schultebraucks, Meng Qian, Duna Abu-Amara, Kelsey Dean, Eugene Laska, Carole Siegel, Aarti Gautam, Guia Guffanti, Rasha Hammamieh, Burook Misganaw, Synthia H. Mellon, Owen M. Wolkowitz, Esther M. Blessing, Amit Etkin, Kerry J. Ressler, Francis J. DoyleIIIIII, Marti Jett & Charles R. Marmar. (2020) Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors. Molecular Psychiatry 26:9, pages 5011-5022.
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Nizam U. Ahamed, Kellen T. Krajewski, Camille C. Johnson, Adam J. Sterczala, Julie P. Greeves, Sophie L. Wardle, Thomas J. O’Leary, Qi Mi, Shawn D. Flanagan, Bradley C. Nindl & Chris Connaboy. (2021) Using Machine Learning and Wearable Inertial Sensor Data for the Classification of Fractal Gait Patterns in Women and Men During Load Carriage. Procedia Computer Science 185, pages 282-291.
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Emel Sari Gokten & Caglar Uyulan. (2021) Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier. Journal of Affective Disorders 279, pages 256-265.
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Jon D Elhai & Christian Montag. (2020) The compatibility of theoretical frameworks with machine learning analyses in psychological research. Current Opinion in Psychology 36, pages 83-88.
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Iuliia Pavlova, Dmytro Zikrach, Dariusz Mosler, Dorota Ortenburger, Tomasz Góra & Jacek Wąsik. (2020) Determinants of anxiety levels among young males in a threat of experiencing military conflict–Applying a machine-learning algorithm in a psychosociological study. PLOS ONE 15:10, pages e0239749.
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Daniel Leightley, Roberto J Rona, James Shearer, Charlotte Williamson, Cerisse Gunasinghe, Amos Simms, Nicola T Fear, Laura Goodwin & Dominic Murphy. (2020) Evaluating the Efficacy of a Mobile App (Drinks:Ration) and Personalized Text and Push Messaging to Reduce Alcohol Consumption in a Veteran Population: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 9:10, pages e19720.
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David A. Ellis. 2020. Smartphones within Psychological Science. Smartphones within Psychological Science.
Daniel Leightley, David Pernet, Sumithra Velupillai, Robert J Stewart, Katharine M Mark, Elena Opie, Dominic Murphy, Nicola T Fear & Sharon A M Stevelink. (2020) The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning. JMIR Medical Informatics 8:5, pages e15852.
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Christopher Burr, Jessica Morley, Mariarosaria Taddeo & Luciano Floridi. (2020) Digital Psychiatry: Risks and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society 1:1, pages 21-33.
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Fenfen Ge, Ying Li, Minlan Yuan, Jun Zhang & Wei Zhang. (2020) Identifying predictors of probable posttraumatic stress disorder in children and adolescents with earthquake exposure: A longitudinal study using a machine learning approach. Journal of Affective Disorders 264, pages 483-493.
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Luis Francisco Ramos-Lima, Vitoria Waikamp, Thyago Antonelli-Salgado, Ives Cavalcante Passos & Lucia Helena Machado Freitas. (2020) The use of machine learning techniques in trauma-related disorders: a systematic review. Journal of Psychiatric Research 121, pages 159-172.
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Anu Priya, Shruti Garg & Neha Prerna Tigga. (2020) Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. Procedia Computer Science 167, pages 1258-1267.
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Katharine M. Mark, Daniel Leightley, David Pernet, Dominic Murphy, Sharon A.M. Stevelink & Nicola T. Fear. (2019) Identifying Veterans Using Electronic Health Records in the United Kingdom: A Feasibility Study. Healthcare 8:1, pages 1.
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Yuriy B. Melnyk, Ihor I. Prykhodko & Anatoliy V. Stadnik. (2019) Medical-psychological support of specialists' professional activity in extreme conditions. Minerva Psichiatrica 60:4.
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Emily Kaczmarek, Alexander Salgo, Hasan Zafari, Leanne Kosowan, Alexander Singer & Farhana Zulkernine. (2019) Diagnosing PTSD using electronic medical records from canadian primary care data. Diagnosing PTSD using electronic medical records from canadian primary care data.
Miseon Shim, Min Jin Jin, Chang-Hwan Im & Seung-Hwan Lee. (2019) Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features. NeuroImage: Clinical 24, pages 102001.
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