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Hu J, Zhao C, Shi C, Zhao Z, Ren Z. Speech-based recognition and estimating severity of PTSD using machine learning. J Affect Disord 2024; 362:859-868. [PMID: 39009320 DOI: 10.1016/j.jad.2024.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 05/31/2024] [Accepted: 07/11/2024] [Indexed: 07/17/2024]
Abstract
BACKGROUND Traditional methodologies for diagnosing post-traumatic stress disorder (PTSD) primarily rely on interviews, incurring considerable costs and lacking objective indices. Integrating biomarkers and machine learning techniques into this diagnostic process has the potential to facilitate accurate PTSD assessment by clinicians. METHODS We assembled a dataset encompassing recordings from 76 individuals diagnosed with PTSD and 60 healthy controls. Leveraging the openSmile framework, we extracted acoustic features from these recordings and employed a random forest algorithm for feature selection. Subsequently, these selected features were utilized as inputs for six distinct classification models and a regression model. RESULTS Classification models employing a feature set of 18 elements yielded robust binary prediction outcomes for PTSD. Notably, the RF model achieved peak accuracy at 0.975 with the highest AUC of 1.0. In terms of the regression model, it exhibited significant predictive capability for PCL-5 scores (MSE = 0.90, MAE = 0.76, R2 = 0.10, p < 0.001). Noteworthy was the correlation coefficient of 0.33 (p < 0.01) between predicted and actual values. LIMITATIONS Firstly, the process of feature selection may compromise the stability of models, which leads to potentially overestimating results. Secondly, it is hard to elucidate the nature of biological mechanisms behind between PTSD patients and healthy individuals. Lastly, the regression model has a limited prediction for PTSD. CONCLUSIONS Distinct speech patterns differentiate PTSD patients and controls. Classification models accurately discern both groups. Regression model gauges PTSD severity, but further validation on larger datasets is needed.
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Affiliation(s)
- Jiawei Hu
- School of Psychology, Central China Normal University, Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China; Key Laboratory of Adolescent CyberPsychology and Behavior(CCNU), National Intelligent Society Governance Experiment Base (Education), Ministry of Education, Wuhan 430079, China
| | - Chunxiao Zhao
- School of Medical Humanities, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Congrong Shi
- School of Educational Science, Anhui Normal University, Wuhu 241000, China
| | - Ziyi Zhao
- School of Psychology, Central China Normal University, Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China; Key Laboratory of Adolescent CyberPsychology and Behavior(CCNU), National Intelligent Society Governance Experiment Base (Education), Ministry of Education, Wuhan 430079, China
| | - Zhihong Ren
- School of Psychology, Central China Normal University, Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China; Key Laboratory of Adolescent CyberPsychology and Behavior(CCNU), National Intelligent Society Governance Experiment Base (Education), Ministry of Education, Wuhan 430079, China.
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Baqir A, Ali M, Jaffar S, Sherazi HHR, Lee M, Bashir AK, Al Dabel MM. Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter. Sci Rep 2024; 14:18902. [PMID: 39143145 PMCID: PMC11325037 DOI: 10.1038/s41598-024-69687-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 08/07/2024] [Indexed: 08/16/2024] Open
Abstract
The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user's PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real-world dataset, we demonstrate our model's effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern.
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Affiliation(s)
- Anees Baqir
- Complex Human Behavior Laboratory, Fondazione Bruno Kessler, Trento, Italy
- Northeastern University, London, UK
| | - Mubashir Ali
- School of Computer Science, University of Birmingham, Birmingham, UK
| | | | | | - Mark Lee
- School of Computer Science, University of Birmingham, Birmingham, UK
| | - Ali Kashif Bashir
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
- Woxsen School of Business, Woxsen University, Hyderabad, 502 345, India
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Maryam M Al Dabel
- Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al-Batin, Saudi Arabia
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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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Ma W, Xu L, Zhang H, Zhang S. Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis. Behav Sci (Basel) 2024; 14:90. [PMID: 38392443 PMCID: PMC10886261 DOI: 10.3390/bs14020090] [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: 12/05/2023] [Revised: 01/21/2024] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
Natural speech plays a pivotal role in communication and interactions between human beings. The prosody of natural speech, due to its high ecological validity and sensitivity, has been acoustically analyzed and more recently utilized in machine learning to identify individuals with autism spectrum disorders (ASDs). In this meta-analysis, we evaluated the findings of empirical studies on acoustic analysis and machine learning techniques to provide statistically supporting evidence for adopting natural speech prosody for ASD detection. Using a random-effects model, the results observed moderate-to-large pooled effect sizes for pitch-related parameters in distinguishing individuals with ASD from their typically developing (TD) counterparts. Specifically, the standardized mean difference (SMD) values for pitch mean, pitch range, pitch standard deviation, and pitch variability were 0.3528, 0.6744, 0.5735, and 0.5137, respectively. However, the differences between the two groups in temporal features could be unreliable, as the SMD values for duration and speech rate were only 0.0738 and -0.0547. Moderator analysis indicated task types were unlikely to influence the final results, whereas age groups showed a moderating role in pooling pitch range differences. Furthermore, promising accuracy rates on ASD identification were shown in our analysis of multivariate machine learning studies, indicating averaged sensitivity and specificity of 75.51% and 80.31%, respectively. In conclusion, these findings shed light on the efficacy of natural prosody in identifying ASD and offer insights for future investigations in this line of research.
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Affiliation(s)
- Wen Ma
- School of Foreign Languages and Literature, Shandong University, Jinan 250100, China
| | - Lele Xu
- School of Foreign Languages and Literature, Shandong University, Jinan 250100, China
| | - Hao Zhang
- School of Foreign Languages and Literature, Shandong University, Jinan 250100, China
| | - Shurui Zhang
- School of Foreign Languages and Literature, Shandong University, Jinan 250100, China
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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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6
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Karstoft KI, Eskelund K, Gradus JL, Andersen SB, Nissen LR. Early prediction of mental health problems following military deployment: Integrating pre- and post-deployment factors in neural network models. J Psychiatr Res 2023; 163:109-117. [PMID: 37209616 DOI: 10.1016/j.jpsychires.2023.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 04/20/2023] [Accepted: 05/01/2023] [Indexed: 05/22/2023]
Abstract
Military personnel deployed to war zones are at increased risk of mental health problems such as posttraumatic stress disorder (PTSD) or depression. Early pre- or post-deployment identification of those at highest risk of such problems is crucial to target intervention to those in need. However, sufficiently accurate models predicting objectively assessed mental health outcomes have not been put forward. In a sample consisting of all Danish military personnel who deployed to war zones for the first (N = 27,594), second (N = 11,083) and third (N = 5,161) time between 1992 and 2013, we apply neural networks to predict psychiatric diagnoses or use of psychotropic medicine in the years following deployment. Models are based on pre-deployment registry data alone or on pre-deployment registry data in combination with post-deployment questionnaire data on deployment experiences or early post-deployment reactions. Further, we identified the most central predictors of importance for the first, second, and third deployment. Models based on pre-deployment registry data alone had lower accuracy (AUCs ranging from 0.61 (third deployment) to 0.67 (first deployment)) than models including pre- and post-deployment data (AUCs ranging from 0.70 (third deployment) to 0.74 (first deployment)). Age at deployment, deployment year and previous physical trauma were important across deployments. Post-deployment predictors varied across deployments but included deployment exposures as well as early post-deployment symptoms. The results suggest that neural network models combining pre- and early post-deployment data can be utilized for screening tools that identify individuals at risk of severe mental health problems in the years following military deployment.
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Affiliation(s)
- Karen-Inge Karstoft
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark; Research and Knowledge Centre, The Danish Veteran Centre, Ringsted, Denmark.
| | - Kasper Eskelund
- Research and Knowledge Centre, The Danish Veteran Centre, Ringsted, Denmark; Center for Applied Audiology Research, Oticon, Kongebakken 9, 2765, Smørum, Denmark.
| | - Jaimie L Gradus
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA; Department of Psychiatry, Psychiatry, Boston University School of Medicine, Boston, MA, USA.
| | - Søren B Andersen
- Research and Knowledge Centre, The Danish Veteran Centre, Ringsted, Denmark.
| | - Lars R Nissen
- Research and Knowledge Centre, The Danish Veteran Centre, Ringsted, Denmark.
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Papadakis N, Havenetidis K, Papadopoulos D, Bissas A. Employing body-fixed sensors and machine learning to predict physical activity in military personnel. BMJ Mil Health 2023; 169:152-156. [PMID: 33127870 DOI: 10.1136/bmjmilitary-2020-001585] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/26/2020] [Accepted: 09/29/2020] [Indexed: 11/03/2022]
Abstract
INTRODUCTION This was a feasibility pilot study aiming to develop and validate an activity recognition system based on a custom-made body-fixed sensor and driven by an algorithm for recognising basic kinetic movements in military personnel. The findings of this study are deemed essential in informing our development process and contributing to our ultimate aim which is to develop a low-cost and easy-to-use body-fixed sensor for military applications. METHODS Fifty military participants performed a series of trials involving walking, running and jumping under laboratory conditions in order to determine the optimal, among five machine learning (ML), classifiers. Thereafter, the accuracy of the classifier was tested towards the prediction of these movements (15 183 measurements) and in relation to participants' gender and fitness level. RESULTS Random forest classifier showed the highest training and validation accuracy (98.5% and 92.9%, respectively) and classified participants with differences in type of activity, gender and fitness level with an accuracy level of 83.6%, 70.0% and 62.2%, respectively. CONCLUSIONS The study showed that accurate prediction of various dynamic activities can be achieved with high sensitivity using a low-cost easy-to-use sensor and a specific ML model. While this technique is in a development stage, our findings demonstrate that our body-fixed sensor prototype alongside a fully trained validated algorithm can strategically support military operations and offer valuable information to commanders controlling operations remotely. Further stages of our developments include the validation of our refined technique on a larger range of military activities and groups by combining activity data with physiological variables to predict phenomena relating to the onset of fatigue and performance decline.
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Affiliation(s)
- Nikolaos Papadakis
- Mathematics & Engineering Sciences, Hellenic Army Academy, Vari, Attiki, Greece
| | - K Havenetidis
- Physical and Cultural Education, Hellenic Army Academy, Vari, Attiki, Greece
| | - D Papadopoulos
- Mathematics & Engineering Sciences, Hellenic Army Academy, Vari, Attiki, Greece
| | - A Bissas
- School of Sport & Exercise, University of Gloucestershire, Gloucester, UK
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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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Leightley D, Murphy D. Personalised digital technology for mental health in the armed forces: the potential, the hype and the dangers. BMJ Mil Health 2023; 169:406-408. [PMID: 36455986 DOI: 10.1136/military-2022-002279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 11/22/2022] [Indexed: 12/03/2022]
Abstract
The COVID-19 pandemic has resulted in a digital technology revolution which included widespread use in remote healthcare settings, remote working and use of technology to support friends and family to stay in touch. The armed forces have also increased its use of digital technology, but not at the same rate, and it is important that they do not fall behind in the revolution. One area where digital technology could be helpful is the treatment and management of mental health conditions. In a civilian setting, digital technology adoption has been found to be acceptable and feasible yet there is little use in the armed forces. In this personal view, we explore the potential use of personalised digital technology for mental health, the hype surrounding it and the dangers.This paper forms part of the special issue of BMJ Military Health dedicated to personalised digital technology for mental health in the armed forces.
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Affiliation(s)
- Daniel Leightley
- King's Centre for Military Health Research, King's College London, London, UK
| | - D Murphy
- Research Department, Combat Stress, Leatherhead, UK
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Iqbal T, Elahi A, Wijns W, Shahzad A. Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:782756. [PMID: 35359827 PMCID: PMC8962952 DOI: 10.3389/fmedt.2022.782756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 02/10/2022] [Indexed: 12/04/2022] Open
Abstract
Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of sensor and reference data during the development phase. One of the most challenging tasks in physiological or pathological stress monitoring is the labeling of the physiological signals collected during an experiment. Commonly, different types of self-reporting questionnaires are used to label the perceived stress instances. These questionnaires only capture stress levels at a specific point in time. Moreover, self-reporting is subjective and prone to inaccuracies. This paper explores the potential feasibility of unsupervised learning clustering classifiers such as Affinity Propagation, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), K-mean, Mini-Batch K-mean, Mean Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) for implementation in stress monitoring wearable devices. Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. The classification results of unsupervised machine learning classifiers are found comparable to supervised machine learning classifiers on two publicly available datasets. The analysis and results of this comparative study demonstrate the potential of unsupervised learning for the development of non-invasive, continuous, and robust detection and monitoring of physiological and pathological stress.
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Affiliation(s)
- Talha Iqbal
- Smart Sensors Lab, Lambe Institute of Translational Research, National University of Ireland Galway, Galway, Ireland
- *Correspondence: Talha Iqbal
| | - Adnan Elahi
- Electrical and Electronics Engineering, National University of Ireland Galway, Galway, Ireland
| | - William Wijns
- Smart Sensors Lab, Lambe Institute of Translational Research, National University of Ireland Galway, Galway, Ireland
| | - Atif Shahzad
- Smart Sensors Lab, Lambe Institute of Translational Research, National University of Ireland Galway, Galway, Ireland
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
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Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/9970363] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. Then, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field.
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12
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Trousset V, Lefèvre T. Artificial Intelligence in Medicine and PTSD. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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13
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Mishra S, Tripathy HK, Kumar Thakkar H, Garg D, Kotecha K, Pandya S. An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment. Front Public Health 2021; 9:795007. [PMID: 34976936 PMCID: PMC8718454 DOI: 10.3389/fpubh.2021.795007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 11/10/2021] [Indexed: 11/20/2022] Open
Abstract
Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health.
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Affiliation(s)
- Sushruta Mishra
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | - Hrudaya Kumar Tripathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India
| | | | - Deepak Garg
- Department of Computer Science and Engineering, School of Engineering and Sciences, Bennett University, Greater Noida, India
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbosis International (Deemed) University, Pune, India
| | - Sharnil Pandya
- Symbiosis Institute of Technology, Symbosis International (Deemed) University, Pune, India
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Zafari H, Kosowan L, Zulkernine F, Signer A. Diagnosing post-traumatic stress disorder using electronic medical record data. Health Informatics J 2021; 27:14604582211053259. [PMID: 34818936 DOI: 10.1177/14604582211053259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
This study proposes a predictive model that uses structured data and unstructured narrative notes from Electronic Medical Records to accurately identify patients diagnosed with Post-Traumatic Stress Disorder (PTSD). We utilize data from primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN) representing 154,118 patients. A reference sample of 195 patients that had their PTSD diagnosis confirmed using a manual chart review of structured data and narrative notes, and PTSD negative patients is used as the gold standard data for model training, validation and testing. We assess structured and unstructured data from eight tables in the MaPCReN namely, patient demographics, disease case, examinations, medication, billing records, health condition, risk factors, and encounter notes. Feature engineering is applied to convert data into proper representation for predictive modeling. We explore serial and parallel mixed data models that are trained on both structured and unstructured data to identify PTSD. Model performances were calculated based on a highly skewed hold-out test dataset. The serial model that uses both structured and text data as input, yielded the highest values in sensitivity (0.77), F-measure (0.76), and AUC (0.88) and the parallel model that uses both structured and text data as the input obtained the highest positive predicted value (PPV) (0.75). Diseases such as PTSD are difficult to diagnose. Information recorded in the chart note over multiple visits of the patients with the primary care physicians has higher predictive power than structured data and combining these two data types can increase the predictive capabilities of machine learning models in diagnosing PTSD. While the deep-learning model outperformed the traditional ensemble model in processing text data, the ensemble classifier obtained better results in ingesting a combination of features obtained from both data types in the serial mixed model. The study demonstrated that unstructured encounter notes enhance a model's ability to identify patients diagnosed with PTSD. These findings can enhance quality improvement, research, and disease surveillance related to PTSD in primary care populations.
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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: 52] [Impact Index Per Article: 17.3] [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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Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts. MATHEMATICS 2021. [DOI: 10.3390/math9060626] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.
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Gokten ES, Uyulan C. Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier. J Affect Disord 2021; 279:256-265. [PMID: 33074145 DOI: 10.1016/j.jad.2020.10.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/29/2020] [Accepted: 10/04/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Depression and post-traumatic stress disorder (PTSD) are among the most common psychiatric disorders observed in children and adolescents exposed to sexual abuse. OBJECTIVE The present study aimed to investigate the effects of many factors such as the characteristics of a child, abuse, and the abuser, family type of the child, and the role of social support in the development of psychiatric disorders using machine learning techniques. PARTICIPANTS AND SETTINGS The records of 482 children and adolescents who were determined to have been sexually abused were examined to predict the development of depression and PTSD. METHODS Each child was evaluated by a child and adolescent psychiatrist in the psychiatric aspect according to the DSM-V. Through the data of both groups, a predictive model was established based on a random forest classifier. RESULTS The mean values and standard deviation of the 10-k cross-validated results were obtained as accuracy: 0.82% (+/- 0.19%), F1: 0.81% (+/- 0.19%), precision: 0.81% (+/- 0.19%), recall: 0.80% (+/- 0.19%) for children with depression; and accuracy: 0.72% (+/- 0.12%), F1: 0.71% (+/- 0.12%), precision: 0.72% (+/- 0.12%), recall: 0.71% (+/- 0.12%) for children with PTSD, respectively. ROC curves were drawn for both, and the AUC results were obtained as 0.88 for major depressive disorder and 0.76 for PTSD. CONCLUSIONS Machine learning techniques are powerful methods that can be used to predict disorders that may develop after sexual abuse. The results should be supported by studies with larger samples, which are repeated and applied to other risk groups.
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Affiliation(s)
- Emel Sari Gokten
- Assoc Prof of Child and Adolescent Psychiatry, Uskudar University Medical Faculty, Istanbul, Turkey.
| | - Caglar Uyulan
- Assist Prof of Mechatronics Engineering Department, Zonguldak Bulent Ecevit University Faculty of Engineering, Zonguldak, Turkey.
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18
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Trousset V, Lefèvre T. Artificial Intelligence in Medicine and PTSD. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_208-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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The compatibility of theoretical frameworks with machine learning analyses in psychological research. Curr Opin Psychol 2020; 36:83-88. [DOI: 10.1016/j.copsyc.2020.05.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 05/12/2020] [Accepted: 05/13/2020] [Indexed: 12/29/2022]
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Pavlova I, Zikrach D, Mosler D, Ortenburger D, Góra T, Wąsik J. 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 2020; 15:e0239749. [PMID: 33027278 PMCID: PMC7540846 DOI: 10.1371/journal.pone.0239749] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 09/11/2020] [Indexed: 11/26/2022] Open
Abstract
Background Anxiety could be felt even in objectively peaceful situations, but a vision of conflict could result in increased stress levels. In this article, we aimed to identify hidden patterns of mental conditions and create male profiles to illustrate the different subgroups as well as determinants of anxiety levels among them in accordance with proximity to a possibility of direct exposure to military action. Methods A sample of Ukrainian males, in duty as conscripts to military service (n = 392, M±SD = 22.1±5.3) participated in a survey. We used the 36-Item Short Form Health Survey, and State-Trait Anxiety Inventory. In addition to psychological indices, social-demographic data were collected. To discover the number of clusters, the k-means algorithm was used, the optimal number of clusters was found by the elbow algorithm. For validation of the model and its use for further prediction, the random forest machine-learning algorithm, was used. Results By performing k-means cluster analyses, 3 subgroups were identified. High values of psychological indices dominated in Subgroup 2, while lowest values dominated in Subgroup 3. Subgroup 1 showed a more even distribution among the indices. The strength of the relevance and main determinants of the prediction of the presented model mostly consisted of mental qualities, while socio-demographic data were slightly significant. Conclusions There is no clear relevance between proximity or even the experience of military actions and anxiety levels. Other factors, mostly subjective feelings about mental conditions, are crucial determinants of feeling anxiety.
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Affiliation(s)
- Iuliia Pavlova
- Theory and Methods of Physical Culture Department, Lviv State University of Physical Culture, Lviv, Ukraine
| | - Dmytro Zikrach
- SoftServe, Lviv, Ukraine
- AiNanoLab, Dubai, United Arab Emirates
| | - Dariusz Mosler
- Department of Health Sciences, Jan Dlugosz University in Czestochowa, Częstochowa, Poland
- * E-mail:
| | - Dorota Ortenburger
- Department of Health Sciences, Jan Dlugosz University in Czestochowa, Częstochowa, Poland
| | - Tomasz Góra
- Department of Health Sciences, Jan Dlugosz University in Czestochowa, Częstochowa, Poland
| | - Jacek Wąsik
- Department of Health Sciences, Jan Dlugosz University in Czestochowa, Częstochowa, Poland
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Leightley D, Rona RJ, Shearer J, Williamson C, Gunasinghe C, Simms A, Fear NT, Goodwin L, Murphy D. 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 Res Protoc 2020; 9:e19720. [PMID: 33006569 PMCID: PMC7568221 DOI: 10.2196/19720] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/10/2020] [Accepted: 08/11/2020] [Indexed: 01/30/2023] Open
Abstract
Background Alcohol misuse is higher in the UK Armed Forces than in the general population. Previous research has shown that interventions delivered via smartphones are efficacious in promoting self-monitoring of alcohol use, have utility in reducing alcohol consumption, and have a broad reach. Objective This single-blinded randomized controlled trial (RCT) aims to assess the efficacy of a 28-day brief alcohol intervention delivered via a smartphone app (Drinks:Ration) in reducing weekly self-reported alcohol consumption between baseline and 3-month follow-up among veterans who drink at a hazardous or harmful level and receive or have received support for mental health symptoms in a clinical setting. Methods In this two-arm, single-blinded RCT, a smartphone app that includes interactive features designed to enhance participants’ motivation and personalized messaging is compared with a smartphone app that provides only government guidance on alcohol consumption. The trial will be conducted in a veteran population that has sought help through Combat Stress, a UK veteran’s mental health charity. Recruitment, consent, and data collection will be carried out automatically through the Drinks:Ration platform. The primary outcome is the change in self-reported weekly alcohol consumption between baseline (day 0) and 3-month follow-up (day 84) as measured using the Time-Line Follow back for Alcohol Consumption. Secondary outcome measures include (1) change in the baseline to 3-month follow-up (day 84) Alcohol Use Disorder Identification Test score and (2) change in the baseline to 3-month follow-up (day 84) World Health Organization Quality of Life-BREF score to assess the quality of adjusted life years. Process evaluation measures include (1) app use and (2) usability ratings as measured by the mHealth App Usability Questionnaire. The primary and secondary outcomes will also be reassessed at the 6-month follow-up (day 168) to assess the longer-term benefits of the intervention, which will be reported as a secondary outcome. Results The study will begin recruitment in October 2020 and is expected to require 12 months to complete. The study results will be published in 2022. Conclusions This study assesses whether a smartphone app is efficacious in reducing self-reported alcohol consumption in a veteran population that has sought help through Combat Stress using personalized messaging and interactive features. This innovative approach, if successful, may provide a means to deliver a low-cost health promotion program that has the potential to reach large groups, in particular those who are geographically dispersed, such as military personnel. Trial Registration ClinicalTrials.gov NCT04494594; https://clinicaltrials.gov/ct2/show/NCT04494594 International Registered Report Identifier (IRRID) PRR1-10.2196/19720
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Affiliation(s)
- Daniel Leightley
- King's College London, King's Centre for Military Health Research, London, United Kingdom
| | - Roberto J Rona
- King's College London, King's Centre for Military Health Research, London, United Kingdom
| | - James Shearer
- King's College London, King's Health Economics, London, United Kingdom
| | | | - Cerisse Gunasinghe
- King's College London, Department of Psychological Medicine, London, United Kingdom
| | - Amos Simms
- Academic Department of Military Mental Health, King's College London, London, United Kingdom.,British Army, London, United Kingdom
| | - Nicola T Fear
- King's College London, King's Centre for Military Health Research, London, United Kingdom.,Academic Department of Military Mental Health, King's College London, London, United Kingdom
| | - Laura Goodwin
- University of Liverpool, Department of Psychological Sciences, Liverpool, United Kingdom
| | - Dominic Murphy
- King's College London, King's Centre for Military Health Research, London, United Kingdom.,Combat Stress, Leatherhead, United Kingdom
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Leightley D, Pernet D, Velupillai S, Stewart RJ, Mark KM, Opie E, Murphy D, Fear NT, Stevelink SAM. The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning. JMIR Med Inform 2020; 8:e15852. [PMID: 32348287 PMCID: PMC7281146 DOI: 10.2196/15852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 12/11/2019] [Accepted: 01/26/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Electronic health care records (EHRs) are a rich source of health-related information, with potential for secondary research use. In the United Kingdom, there is no national marker for identifying those who have previously served in the Armed Forces, making analysis of the health and well-being of veterans using EHRs difficult. OBJECTIVE This study aimed to develop a tool to identify veterans from free-text clinical documents recorded in a psychiatric EHR database. METHODS Veterans were manually identified using the South London and Maudsley (SLaM) Biomedical Research Centre Clinical Record Interactive Search-a database holding secondary mental health care electronic records for the SLaM National Health Service Foundation Trust. An iterative approach was taken; first, a structured query language (SQL) method was developed, which was then refined using natural language processing and machine learning to create the Military Service Identification Tool (MSIT) to identify if a patient was a civilian or veteran. Performance, defined as correct classification of veterans compared with incorrect classification, was measured using positive predictive value, negative predictive value, sensitivity, F1 score, and accuracy (otherwise termed Youden Index). RESULTS A gold standard dataset of 6672 free-text clinical documents was manually annotated by human coders. Of these documents, 66.00% (4470/6672) were then used to train the SQL and MSIT approaches and 34.00% (2202/6672) were used for testing the approaches. To develop the MSIT, an iterative 2-stage approach was undertaken. In the first stage, an SQL method was developed to identify veterans using a keyword rule-based approach. This approach obtained an accuracy of 0.93 in correctly predicting civilians and veterans, a positive predictive value of 0.81, a sensitivity of 0.75, and a negative predictive value of 0.95. This method informed the second stage, which was the development of the MSIT using machine learning, which, when tested, obtained an accuracy of 0.97, a positive predictive value of 0.90, a sensitivity of 0.91, and a negative predictive value of 0.98. CONCLUSIONS The MSIT has the potential to be used in identifying veterans in the United Kingdom from free-text clinical documents, providing new and unique insights into the health and well-being of this population and their use of mental health care services.
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Affiliation(s)
- Daniel Leightley
- King's Centre for Military Health Research, King's College London, London, United Kingdom
| | - David Pernet
- King's Centre for Military Health Research, King's College London, London, United Kingdom
| | - Sumithra Velupillai
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Robert J Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Katharine M Mark
- King's Centre for Military Health Research, King's College London, London, United Kingdom
| | - Elena Opie
- King's Centre for Military Health Research, King's College London, London, United Kingdom
| | - Dominic Murphy
- King's Centre for Military Health Research, King's College London, London, United Kingdom
- Combat Stress, Letherhead, United Kingdom
| | - Nicola T Fear
- King's Centre for Military Health Research, King's College London, London, United Kingdom
- Academic Department of Military Mental Health, King's College London, London, United Kingdom
| | - Sharon A M Stevelink
- King's Centre for Military Health Research, King's College London, London, United Kingdom
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Burr C, Morley J, Taddeo M, Floridi L. Digital Psychiatry: Risks and Opportunities for Public Health and Wellbeing. ACTA ACUST UNITED AC 2020. [DOI: 10.1109/tts.2020.2977059] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Ge F, Li Y, Yuan M, Zhang J, Zhang W. Identifying predictors of probable posttraumatic stress disorder in children and adolescents with earthquake exposure: A longitudinal study using a machine learning approach. J Affect Disord 2020; 264:483-493. [PMID: 31759663 DOI: 10.1016/j.jad.2019.11.079] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 09/30/2019] [Accepted: 11/12/2019] [Indexed: 01/09/2023]
Abstract
BACKGROUND Evidence has identified risk factors associated with individuals with trauma exposure who develop posttraumatic stress disorder (PTSD). How to combine risk factors to predict probable PTSD in young survivors using machine learning is limited. The study aimed to integrated multiple measures at 2 weeks after the earthquake using machine learning for the prediction of probable PTSD at 3 months after earthquake. METHODS A total of 2099 young survivors with earthquake exposure were included. We integrated multiple domains of variables to 'train' a machine learning algorithm (XGBoost). Thirty-one combination types were implemented and evaluated. The resulting XGBoost was utilized in identifying individual participants as either probable PTSD or no PTSD. RESULTS Any combination type predicted young survivor probable PTSD, with prediction accuracies ranging between 66%-80% (p < 0.05). In particular, the combination of earthquake experience, everyday functioning, somatic symptoms and sleeping correctly predicted 683 out of 802 cases of probable PTSD, translating to a classical accuracy of 74.476% (85.156% sensitivity and 60.366% specificity) and an area under the curve of 0.80. The most relevant variables (e.g. age, sex, property loss and a sedentary lifestyle) revealed in the present study. LIMITATIONS Participants from a specific district might limit the generalizability of our results. Self-report questionnaires and non-standardized measures were used to assess symptoms. CONCLUSION Detection of probable PTSD according to self-reported measurement data is feasible, may improve operational efficiencies via enabling targeted intervention, before manifestation of symptoms.
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Affiliation(s)
- Fenfen Ge
- Mental Health Center of West China Hospital and Disaster Medicine Center, Sichuan University, Chengdu 610041 Sichuan, P. R. China.
| | - Ying Li
- Embedded System and Intelligent Computing Laboratory, University of Electronic Science and Technology of China, Chengdu 610041 Sichuan, P. R. China.
| | - Minlan Yuan
- Mental Health Center of West China Hospital, Sichuan University, Chengdu 610041 Sichuan, P. R. China.
| | - Jun Zhang
- Mental Health Center of West China Hospital and Disaster Medicine Center, Sichuan University, Chengdu 610041 Sichuan, P. R. China.
| | - Wei Zhang
- Mental Health Center of West China Hospital, Sichuan University, Chengdu 610041 Sichuan, P. R. China.
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Ramos-Lima LF, Waikamp V, Antonelli-Salgado T, Passos IC, Freitas LHM. The use of machine learning techniques in trauma-related disorders: a systematic review. J Psychiatr Res 2020; 121:159-172. [PMID: 31830722 DOI: 10.1016/j.jpsychires.2019.12.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 11/22/2019] [Accepted: 12/05/2019] [Indexed: 12/27/2022]
Abstract
Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice and in academic research, due to clinical and biological heterogeneity. Machine learning (ML) techniques can be applied to improve classification of disorders, to predict outcomes or to determine person-specific treatment selection. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with ASD or PTSD. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to May 2019. We found 806 abstracts and included 49 studies in our review. Most of the included studies used multiple levels of biological data to predict risk factors or to identify early symptoms related to PTSD. Other studies used ML classification techniques to distinguish individuals with ASD or PTSD from other psychiatric disorder or from trauma-exposed and healthy controls. We also found studies that attempted to define outcome profiles using clustering techniques and studies that assessed the relationship among symptoms using network analysis. Finally, we proposed a quality assessment in this review, evaluating methodological and technical features on machine learning studies. We concluded that etiologic and clinical heterogeneity of ASD/PTSD patients is suitable to machine learning techniques and a major challenge for the future is to use it in clinical practice for the benefit of patients in an individual level.
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Affiliation(s)
- Luis Francisco Ramos-Lima
- Post-graduate Program in Psychiatry and Behavioral Sciences, Federal University at Rio Grande do Sul, Porto Alegre, Brazil; Psychological Trauma Research and Treatment Program (NET-Trauma), Clinical Hospital of Porto Alegre, Porto Alegre, Brazil.
| | - Vitoria Waikamp
- Post-graduate Program in Psychiatry and Behavioral Sciences, Federal University at Rio Grande do Sul, Porto Alegre, Brazil; Psychological Trauma Research and Treatment Program (NET-Trauma), Clinical Hospital of Porto Alegre, Porto Alegre, Brazil
| | - Thyago Antonelli-Salgado
- Bipolar Disorder Program, Laboratory of Molecular Psychiatry, Clinical Hospital of Porto Alegre, Porto Alegre, Brazil
| | - Ives Cavalcante Passos
- Post-graduate Program in Psychiatry and Behavioral Sciences, Federal University at Rio Grande do Sul, Porto Alegre, Brazil; Bipolar Disorder Program, Laboratory of Molecular Psychiatry, Clinical Hospital of Porto Alegre, Porto Alegre, Brazil
| | - Lucia Helena Machado Freitas
- Post-graduate Program in Psychiatry and Behavioral Sciences, Federal University at Rio Grande do Sul, Porto Alegre, Brazil; Psychological Trauma Research and Treatment Program (NET-Trauma), Clinical Hospital of Porto Alegre, Porto Alegre, Brazil
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Carr S. 'AI gone mental': engagement and ethics in data-driven technology for mental health. J Ment Health 2020; 29:125-130. [PMID: 32000544 DOI: 10.1080/09638237.2020.1714011] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Sarah Carr
- Senior Fellow in Mental Health Policy, University of Birmingham, Edgbaston, Birmingham
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Priya A, Garg S, Tigga NP. Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.procs.2020.03.442] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Zhao M, Feng Z. Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study. Neuropsychiatr Dis Treat 2020; 16:2743-2752. [PMID: 33209029 PMCID: PMC7669500 DOI: 10.2147/ndt.s275620] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 10/19/2020] [Indexed: 11/23/2022] Open
Abstract
PURPOSE Traditional questionnaires assessing the severity of depression are limited and might not be appropriate for military personnel. We intend to explore the diagnostic ability of three machine learning methods for evaluating the depression status of Chinese recruits, using the Chinese version of Beck Depression Inventory-II (BDI-II) as the standard. PATIENTS AND METHODS Our diagnostic study was carried out in Luoyang City (Henan Province, China; 10/16/2018-12/10/2018) with a sample of 1000 Chinese male recruits selected using cluster convenient sampling. All participants completed the BDI and 3 questionnaires including the data of demographics, military careers and 18 factors. The participants were randomly selected as the training set and the testing at 2:1. The machine learning methods tested for assessing the presence or absence of depression status were neural network (NN), support vector machine (SVM), and decision tree (DT). RESULTS A total of 1000 participants completed the questionnaires, with 223 reporting depression status and 777 not. The highest sensitivity was observed for DT (94.1%), followed by SVM (93.4%) and NN (93.1%). The highest specificity was observed for NN (60.0%), followed by SVM (58.8%) and DT (43.3%). The area under the curve (AUC) of the SVM was the largest (0.862) compared with NN (0.860) and DT (0.734). The regression prediction error and error volatility of the SVM were the smallest. CONCLUSION The SVM has the smallest prediction error and error volatility, as well as the largest AUC compared with NN and DT for assessing the presence or absence of depression status in Chinese recruits.
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Affiliation(s)
- Mengxue Zhao
- Department of Military Psychology, Faculty of Medical Psychology, Army Medical University, Chongqing, People's Republic of China
| | - Zhengzhi Feng
- Faculty of Medical Psychology, Army Medical University, Chongqing, People's Republic of China
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M. Mark K, Leightley D, Pernet D, Murphy D, Stevelink SA, T. Fear N. Identifying Veterans Using Electronic Health Records in the United Kingdom: A Feasibility Study. Healthcare (Basel) 2019; 8:healthcare8010001. [PMID: 31861575 PMCID: PMC7151350 DOI: 10.3390/healthcare8010001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/16/2019] [Accepted: 12/17/2019] [Indexed: 01/01/2023] Open
Abstract
There is a lack of quantitative evidence concerning UK (United Kingdom) Armed Forces (AF) veterans who access secondary mental health care services-specialist care often delivered in high intensity therapeutic clinics or hospitals-for their mental health difficulties. The current study aimed to investigate the utility and feasibility of identifying veterans accessing secondary mental health care services using National Health Service (NHS) electronic health records (EHRs) in the UK. Veterans were manually identified using the Clinical Record Interactive Search (CRIS) system-a database holding secondary mental health care EHRs for an NHS Trust in the UK. We systematically and manually searched CRIS for veterans, by applying a military-related key word search strategy to the free-text clinical notes completed by clinicians. Relevant data on veterans' socio-demographic characteristics, mental disorder diagnoses and treatment pathways through care were extracted for analysis. This study showed that it is feasible, although time consuming, to identify veterans through CRIS. Using the military-related key word search strategy identified 1600 potential veteran records. Following manual review, 693 (43.3%) of these records were verified as "probable" veterans and used for analysis. They had a median age of 74 years (interquartile range (IQR): 53-86); the majority were male (90.8%) and lived alone (38.0%). The most common mental diagnoses overall were depressive disorders (22.9%), followed by alcohol use disorders (10.5%). Differences in care pathways were observed between pre and post national service (NS) era veterans. This feasibility study represents a first step in showing that it is possible to identify veterans through free-text clinical notes. It is also the first to compare veterans from pre and post NS era.
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Affiliation(s)
- Katharine M. Mark
- King’s Centre for Military Health Research, King’s College London, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK (D.L.); (D.P.); (D.M.); (N.T.F.)
| | - Daniel Leightley
- King’s Centre for Military Health Research, King’s College London, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK (D.L.); (D.P.); (D.M.); (N.T.F.)
| | - David Pernet
- King’s Centre for Military Health Research, King’s College London, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK (D.L.); (D.P.); (D.M.); (N.T.F.)
| | - Dominic Murphy
- King’s Centre for Military Health Research, King’s College London, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK (D.L.); (D.P.); (D.M.); (N.T.F.)
- Combat Stress, Tyrwhitt House, Oaklawn Road, Leatherhead KT22 0BX, UK
| | - Sharon A.M. Stevelink
- King’s Centre for Military Health Research, King’s College London, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK (D.L.); (D.P.); (D.M.); (N.T.F.)
- Department of Psychological Medicine, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London SE5 8AF, UK
- Correspondence: ; Tel.: +44-(0)20-7848-5817
| | - Nicola T. Fear
- King’s Centre for Military Health Research, King’s College London, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK (D.L.); (D.P.); (D.M.); (N.T.F.)
- Academic Department of Military Mental Health, King’s College London, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK
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Shim M, Jin MJ, Im CH, Lee SH. Machine-learning-based classification between post-traumatic stress disorder and major depressive disorder using P300 features. NEUROIMAGE-CLINICAL 2019; 24:102001. [PMID: 31627171 PMCID: PMC6812119 DOI: 10.1016/j.nicl.2019.102001] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 08/29/2019] [Accepted: 09/02/2019] [Indexed: 11/03/2022]
Abstract
BACKGROUND The development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for precise diagnosis. We investigated whether P300 features in both sensor-level and source-level could be effectively used to classify post-traumatic stress disorder (PTSD) and major depressive disorder (MDD). METHOD EEG signals were recorded from fifty-one PTSD patients, 67 MDD patients, and 39 healthy controls (HCs) while performing an auditory oddball task. Amplitude and latency of P300 were evaluated, and the current source analysis of P300 components was conducted using sLORETA. Finally, we classified two groups using machine-learning methods with both sensor- and source-level features. Moreover, we checked the comorbidity effects using the same approaches (PTSD-mono diagnosis (PTSDm, n = 28) and PTSD-comorbid diagnosis (PTSDc, n = 23)). RESULTS PTSD showed significantly reduced P300 amplitudes and prolonged latency compared to HCs and MDD. Moreover, PTSD showed significantly reduced source activities, and the source activities were significantly correlated with symptoms of depression and anxiety. Also, the best classification accuracy at each pair was as follows: 80.00% (PTSD-HCs), 67.92% (MDD-HCs), 70.34% (PTSD-MDD), 82.09% (PTSDm-HCs), 71.58% (PTSDm-MDD), 82.56% (PTSDc-HCs), and 76.67% (PTSDc- MDD). CONCLUSION Since abnormal P300 reflects pathophysiological characteristics of PTSD, PTSD patients were well-discriminated from MDD and HCs when using P300 features. Thus, altered P300 characteristics in both sensor- and source-level may be useful biomarkers to diagnosis PTSD.
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Affiliation(s)
- Miseon Shim
- Department of Biomedical Sciences, University of Missouri, Kansas City, USA; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea
| | - Min Jin Jin
- Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea; Department of Psychology, Chung-Ang University, Seoul, Republic of Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea; Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, Republic of Korea.
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Wickersham A, Petrides PM, Williamson V, Leightley D. Efficacy of mobile application interventions for the treatment of post-traumatic stress disorder: A systematic review. Digit Health 2019; 5:2055207619842986. [PMID: 31019722 PMCID: PMC6463234 DOI: 10.1177/2055207619842986] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Accepted: 03/19/2019] [Indexed: 11/27/2022] Open
Abstract
Background Many adults with post-traumatic stress disorder (PTSD) are unable to access healthcare services for treatment due to logistical, social, and attitudinal barriers. Interventions delivered via mobile applications (apps) may help overcome these barriers. Objective The aim of this study is to systematically evaluate the most recent evidence from trials investigating the efficacy of mobile apps for treating PTSD. Methods PubMed, Web of Science, Embase, PsycINFO, and Medline were searched in February 2018. Randomised controlled trials (RCTs) were included if they quantitatively evaluated the efficacy of a mobile app for treating PTSD as part of the primary aim. Findings were presented in a narrative synthesis. Results In the five identified RCTs, the use of app-based interventions appeared to be associated with reductions in PTSD symptoms. However, the strength of evidence for this association appeared to be inconsistent, and there was little evidence that those using the apps experienced greater reductions in PTSD symptoms than those in control conditions. Nonetheless, there was some evidence that app-based interventions are both a feasible and acceptable treatment pathway option. Conclusions Included studies were often limited by small sample sizes, brief intervention, and follow-up periods, and self-reported measures of PTSD. Evidence for the efficacy of mobile interventions for treating PTSD was inconclusive, but promising. Healthcare professionals should exercise caution in recommending app-based interventions until the potentially adverse effects of app use are better understood and larger-scale studies have taken place.
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Affiliation(s)
- Alice Wickersham
- King's Centre for Military Health Research, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Petros Minas Petrides
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Victoria Williamson
- King's Centre for Military Health Research, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Leightley
- King's Centre for Military Health Research, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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32
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Affiliation(s)
- Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London,London, UK
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33
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Affiliation(s)
- Martin Guha
- Maudsley Philosophy Group, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
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