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
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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. / Karstoft, Karen Inge; Tsamardinos, Ioannis; Eskelund, Kasper; Andersen, Søren Bo; Nissen, Lars Ravnborg.
I: J M I R Medical Informatics, Bind 8, Nr. 7, e17119, 07.2020.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Applicability of an automated model and parameter selection in the prediction of screening-level PTSD in Danish soldiers following deployment
T2 - Development study of transferable predictive models using automated machine learning
AU - Karstoft, Karen Inge
AU - Tsamardinos, Ioannis
AU - Eskelund, Kasper
AU - Andersen, Søren Bo
AU - Nissen, Lars Ravnborg
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Decision support
KW - Machine learning
KW - Mental health
KW - Military
KW - PTSD
KW - Screening
U2 - 10.2196/17119
DO - 10.2196/17119
M3 - Journal article
C2 - 32706722
AN - SCOPUS:85097464075
VL - 8
JO - JMIR Medical Informatics
JF - JMIR Medical Informatics
SN - 2291-9694
IS - 7
M1 - e17119
ER -
ID: 254521439