Bridging a translational gap: using machine learning to improve the prediction of PTSD.

Journal: BMC psychiatry
PMID: 25886446

Abstract

BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators.

Authors

  • Karen-Inge Karstoft
    Research and Knowledge Centre, Danish Veteran Centre, Garnisonen 1, 4100, Ringsted, Denmark. kikarstoft@health.sdu.dk.
  • Isaac R Galatzer-Levy
    Department of Psychiatry, NYU School of Medicine, New York, NY, USA. Isaac.Galatzer-Levy@nyumc.org.
  • Alexander Statnikov
    Center for Health Informatics and Bioinformatics, NYU School of Medicine, New York, NY, USA. Alexander.Statnikov@nyumc.org.
  • Zhiguo Li
    Key Laboratory of Clean Energy Materials Chemistry of Guangdong Higher Education Institutes, School of Chemistry and Chemical Engineering, Lingnan Normal University, Zhanjiang 524048, China.
  • Arieh Y Shalev
    Department of Psychiatry, NYU School of Medicine, New York, NY, USA. Arieh.shalev@nyumc.org.

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