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Somé NH, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Front Psychiatry 2024; 15:1291362. [PMID: 38501090 PMCID: PMC10944962 DOI: 10.3389/fpsyt.2024.1291362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Background Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature. Objective Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified. Methods A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed. Results The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study. Conclusion The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022333454 identifier CRD42022333454.
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Affiliation(s)
- Nibene H. Somé
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Pardis Noormohammadpour
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Shannon Lange
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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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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Papini S, Norman SB, Campbell-Sills L, Sun X, He F, Kessler RC, Ursano RJ, Jain S, Stein MB. Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment. JAMA Netw Open 2023; 6:e2321273. [PMID: 37389870 PMCID: PMC10314304 DOI: 10.1001/jamanetworkopen.2023.21273] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/16/2023] [Indexed: 07/01/2023] Open
Abstract
Importance Military deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience. Objective To develop and validate a machine learning (ML) model to predict postdeployment PTSD. Design, Setting, and Participants This diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022. Main Outcomes and Measures Posttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse. Results This study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion. Conclusions and Relevance In this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.
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Affiliation(s)
- Santiago Papini
- Department of Psychiatry, University of California, San Diego, La Jolla
- Division of Research, Kaiser Permanente Northern California, Oakland
| | - Sonya B. Norman
- Department of Psychiatry, University of California, San Diego, La Jolla
- National Center for PTSD, White River Junction, Vermont
- Veterans Affairs Center of Excellence for Stress and Mental Health, San Diego, California
| | | | - Xiaoying Sun
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Feng He
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Robert J. Ursano
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Sonia Jain
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
| | - Murray B. Stein
- Department of Psychiatry, University of California, San Diego, La Jolla
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla
- Psychiatry Service, Veterans Affairs San Diego Healthcare System, San Diego, California
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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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Campbell-Sills L, Kautz JD, Choi KW, Naifeh JA, Aliaga PA, Jain S, Sun X, Kessler RC, Stein MB, Ursano RJ, Bliese PD. Effects of prior deployments and perceived resilience on anger trajectories of combat-deployed soldiers. Psychol Med 2023; 53:2031-2040. [PMID: 34802475 PMCID: PMC9124235 DOI: 10.1017/s0033291721003779] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/26/2021] [Accepted: 09/01/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND Problematic anger is frequently reported by soldiers who have deployed to combat zones. However, evidence is lacking with respect to how anger changes over a deployment cycle, and which factors prospectively influence change in anger among combat-deployed soldiers. METHODS Reports of problematic anger were obtained from 7298 US Army soldiers who deployed to Afghanistan in 2012. A series of mixed-effects growth models estimated linear trajectories of anger over a period of 1-2 months before deployment to 9 months post-deployment, and evaluated the effects of pre-deployment factors (prior deployments and perceived resilience) on average levels and growth of problematic anger. RESULTS A model with random intercepts and slopes provided the best fit, indicating heterogeneity in soldiers' levels and trajectories of anger. First-time deployers reported the lowest anger overall, but the most growth in anger over time. Soldiers with multiple prior deployments displayed the highest anger overall, which remained relatively stable over time. Higher pre-deployment resilience was associated with lower reports of anger, but its protective effect diminished over time. First- and second-time deployers reporting low resilience displayed different anger trajectories (stable v. decreasing, respectively). CONCLUSIONS Change in anger from pre- to post-deployment varies based on pre-deployment factors. The observed differences in anger trajectories suggest that efforts to detect and reduce problematic anger should be tailored for first-time v. repeat deployers. Ongoing screening is needed even for soldiers reporting high resilience before deployment, as the protective effect of pre-deployment resilience on anger erodes over time.
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Affiliation(s)
| | - Jason D. Kautz
- Department of Organizations, Strategy, and International Management, University of Texas at Dallas, Dallas, TX, USA
| | - Karmel W. Choi
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Boston, MA, USA
| | - James A. Naifeh
- Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Pablo A. Aliaga
- Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Sonia Jain
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Xiaoying Sun
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Murray B. Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Robert J. Ursano
- Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Paul D. Bliese
- Department of Management, Darla Moore School of Business, University of South Carolina, Columbia, SC, USA
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Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Ment Health 2023; 10:e42045. [PMID: 36729567 PMCID: PMC9936371 DOI: 10.2196/42045] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/02/2022] [Accepted: 11/20/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.
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Affiliation(s)
- Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Ledia Lazeri
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
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Abstract
The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.
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Wardenaar KJ, Riese H, Giltay EJ, Eikelenboom M, van Hemert AJ, Beekman AF, Penninx BWJH, Schoevers RA. Common and specific determinants of 9-year depression and anxiety course-trajectories: A machine-learning investigation in the Netherlands Study of Depression and Anxiety (NESDA). J Affect Disord 2021; 293:295-304. [PMID: 34225209 DOI: 10.1016/j.jad.2021.06.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 01/06/2023]
Abstract
BACKGROUND Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to identify and compare the main determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity. METHODS Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (class-probability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error) combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were identified by determining variable-importance and correlations between each SL-predicted and observed outcome (ρpred) were calculated. RESULTS Low to high prediction correlations (ρpred: 0.41-0.91, median=0.73) were found. In the SL, important determinants of psychological distress were age, young age of onset, respiratory rate, participation disability, somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress. LIMITATIONS Limited sample size for machine learning. CONCLUSIONS The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course.
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Affiliation(s)
- Klaas J Wardenaar
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Groningen, The Netherlands.
| | - Harriëtte Riese
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Groningen, The Netherlands
| | - Erik J Giltay
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Merijn Eikelenboom
- Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Albert J van Hemert
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Aartjan F Beekman
- Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Brenda W J H Penninx
- Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Robert A Schoevers
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Groningen, The Netherlands
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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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10
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Bernert RA, Hilberg AM, Melia R, Kim JP, Shah NH, Abnousi F. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E5929. [PMID: 32824149 PMCID: PMC7460360 DOI: 10.3390/ijerph17165929] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022]
Abstract
Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.
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Affiliation(s)
- Rebecca A. Bernert
- Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Amanda M. Hilberg
- Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Ruth Melia
- Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
- Department of Psychology, National University of Ireland, Galway, Ireland
| | - Jane Paik Kim
- Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Nigam H. Shah
- Department of Medicine, Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94304, USA
- Informatics, Stanford Center for Clinical and Translational Research, and Education (Spectrum), Stanford University, Stanford CA 94304, USA
| | - Freddy Abnousi
- Facebook, Menlo Park, CA 94025, USA
- Yale University School of Medicine, New Haven, CT 06510, USA
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11
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Karstoft KI, Tsamardinos I, Eskelund K, Andersen SB, Nissen LR. Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning. JMIR Med Inform 2020; 8:e17119. [PMID: 32706722 PMCID: PMC7407253 DOI: 10.2196/17119] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 03/30/2020] [Accepted: 04/16/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is a relatively common consequence of deployment to war zones. Early postdeployment screening with the aim of identifying those at risk for PTSD in the years following deployment will help deliver interventions to those in need but have so far proved unsuccessful. OBJECTIVE This study aimed to test the applicability of automated model selection and the ability of automated machine learning prediction models to transfer across cohorts and predict screening-level PTSD 2.5 years and 6.5 years after deployment. METHODS Automated machine learning was applied to data routinely collected 6-8 months after return from deployment from 3 different cohorts of Danish soldiers deployed to Afghanistan in 2009 (cohort 1, N=287 or N=261 depending on the timing of the outcome assessment), 2010 (cohort 2, N=352), and 2013 (cohort 3, N=232). RESULTS Models transferred well between cohorts. For screening-level PTSD 2.5 and 6.5 years after deployment, random forest models provided the highest accuracy as measured by area under the receiver operating characteristic curve (AUC): 2.5 years, AUC=0.77, 95% CI 0.71-0.83; 6.5 years, AUC=0.78, 95% CI 0.73-0.83. Linear models performed equally well. Military rank, hyperarousal symptoms, and total level of PTSD symptoms were highly predictive. CONCLUSIONS Automated machine learning provided validated models that can be readily implemented in future deployment cohorts in the Danish Defense with the aim of targeting postdeployment support interventions to those at highest risk for developing PTSD, provided the cohorts are deployed on similar missions.
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Affiliation(s)
- Karen-Inge Karstoft
- Research and Knowledge Centre, The Danish Veterans Centre, Ringsted, Denmark.,Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete, Heraklion, Crete, Greece.,Gnosis Data Analysis PC, Heraklion, Greece
| | - Kasper Eskelund
- Research and Knowledge Centre, The Danish Veterans Centre, Ringsted, Denmark.,Department of Military Psychology, The Danish Veterans Centre, Copenhagen, Denmark
| | - Søren Bo Andersen
- Research and Knowledge Centre, The Danish Veterans Centre, Ringsted, Denmark
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12
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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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13
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Zuromski KL, Bernecker SL, Chu C, Wilks CR, Gutierrez PM, Joiner TE, Liu H, Naifeh JA, Nock MK, Sampson NA, Zaslavsky AM, Stein MB, Ursano RJ, Kessler RC. Pre-deployment predictors of suicide attempt during and after combat deployment: Results from the Army Study to Assess Risk and Resilience in Servicemembers. J Psychiatr Res 2020; 121:214-221. [PMID: 31865211 PMCID: PMC6953717 DOI: 10.1016/j.jpsychires.2019.12.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 11/04/2019] [Accepted: 12/05/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND Deployment-related experiences might be risk factors for soldier suicides, in which case identification of vulnerable soldiers before deployment could inform preventive efforts. We investigated this possibility by using pre-deployment survey and administrative data in a sample of US Army soldiers to develop a risk model for suicide attempt (SA) during and shortly after deployment. METHODS Data came from the Army Study to Assess Risk and Resilience in Servicemembers Pre-Post Deployment Survey (PPDS). Soldiers completed a baseline survey shortly before deploying to Afghanistan in 2011-2012. Survey measures were used to predict SAs, defined using administrative and subsequent survey data, through 30 months after deployment. Models were built using penalized regression and ensemble machine learning methods. RESULTS Significant pre-deployment risk factors were history of traumatic brain injury, 9 + mental health treatment visits in the 12 months before deployment, young age, female, previously married, and low relationship quality. Cross-validated AUC of the best penalized and ensemble models were .75-.77. 21.3-40.4% of SAs occurred among the 5-10% of soldiers with highest predicted risk and positive predictive value (PPV) among these high-risk soldiers was 4.4-5.7%. CONCLUSIONS SA can be predicted significantly from pre-deployment data, but intervention planning needs to take PPV into consideration.
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Affiliation(s)
- Kelly L. Zuromski
- Department of Psychology, Harvard University, Cambridge, MA, USA,Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Samantha L. Bernecker
- Department of Psychology, Harvard University, Cambridge, MA, USA,Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Carol Chu
- Department of Psychology, Harvard University, Cambridge, MA, USA,Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Chelsey R. Wilks
- Department of Psychology, Harvard University, Cambridge, MA, USA,Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Peter M. Gutierrez
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO, USA,Rocky Mountain Mental Illness Research, Education, and Clinical Center, Rocky Mountain Regional Veterans Affairs Medical Center, Aurora, CO, USA
| | - Thomas E. Joiner
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Howard Liu
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - James A. Naifeh
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine, Bethesda, MD, USA
| | - Matthew K. Nock
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Nancy A. Sampson
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Alan M. Zaslavsky
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Murray B. Stein
- Departments of Psychiatry and Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Robert J. Ursano
- Center for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine, Bethesda, MD, USA
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
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14
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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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15
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Stein MB, Choi KW, Jain S, Campbell-Sills L, Chen CY, Gelernter J, He F, Heeringa SG, Maihofer AX, Nievergelt C, Nock MK, Ripke S, Sun X, Kessler RC, Smoller JW, Ursano RJ. Genome-wide analyses of psychological resilience in U.S. Army soldiers. Am J Med Genet B Neuropsychiatr Genet 2019; 180:310-319. [PMID: 31081985 PMCID: PMC6551278 DOI: 10.1002/ajmg.b.32730] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 04/09/2019] [Accepted: 04/11/2019] [Indexed: 12/26/2022]
Abstract
Though a growing body of preclinical and translational research is illuminating a biological basis for resilience to stress, little is known about the genetic basis of psychological resilience in humans. We conducted genome-wide association studies (GWASs) of self-assessed (by questionnaire) and outcome-based (incident mental disorders from predeployment to postdeployment) resilience among European (EUR) ancestry soldiers in the Army study to assess risk and resilience in servicemembers. Self-assessed resilience (N = 11,492) was found to have significant common-variant heritability (h2 = 0.162, se = 0.050, p = 5.37 × 10-4 ), and to be significantly negatively genetically correlated with neuroticism (rg = -0.388, p = .0092). GWAS results from the EUR soldiers revealed a genome-wide significant locus on an intergenic region on Chr 4 upstream from doublecortin-like kinase 2 (DCLK2) (four single nucleotide polymorphisms (SNPs) in LD; top SNP: rs4260523 [p = 5.65 × 10-9 ] is an eQTL in frontal cortex), a member of the doublecortin family of kinases that promote survival and regeneration of injured neurons. A second gene, kelch-like family member 36 (KLHL36) was detected at gene-wise genome-wide significance [p = 1.89 × 10-6 ]. A polygenic risk score derived from the self-assessed resilience GWAS was not significantly associated with outcome-based resilience. In very preliminary results, genome-wide significant association with outcome-based resilience was found for one locus (top SNP: rs12580015 [p = 2.37 × 10-8 ]) on Chr 12 downstream from solute carrier family 15 member 5 (SLC15A5) in subjects (N = 581) exposed to the highest level of deployment stress. The further study of genetic determinants of resilience has the potential to illuminate the molecular bases of stress-related psychopathology and point to new avenues for therapeutic intervention.
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Affiliation(s)
- Murray B. Stein
- Department of Psychiatry, University of California San Diego, La Jolla, California,Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California,Psychiatry Service, VA San Diego Healthcare System, San Diego, California
| | - Karmel W. Choi
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Sonia Jain
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California
| | - Laura Campbell-Sills
- Department of Psychiatry, University of California San Diego, La Jolla, California
| | - Chia-Yen Chen
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts,Department of Psychiatry, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts
| | - Joel Gelernter
- Department of Psychiatry, Yale University, New Haven, Connecticut,VA Connecticut Healthcare System, West Haven, Connecticut,Departments of Genetics and Neurobiology, Yale University, New Haven, Connecticut
| | - Feng He
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California
| | - Steven G. Heeringa
- Institute for Social Research, University of Michigan, Ann Arbor, Michigan
| | - Adam X. Maihofer
- Department of Psychiatry, University of California San Diego, La Jolla, California
| | - Caroline Nievergelt
- Department of Psychiatry, University of California San Diego, La Jolla, California
| | - Matthew K. Nock
- Department of Psychology, Harvard University, Cambridge, Massachusetts
| | - Stephan Ripke
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts,Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston MA 02114, USA,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin 10117, Germany
| | - Xiaoying Sun
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts,Department of Psychiatry, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts
| | - Robert J. Ursano
- Department of Psychiatry, Uniformed Services University of the Health Sciences, Bethesda, Maryland
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