A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders

AMIA Annu Symp Proc. 2017 Feb 10;2016:837-843. eCollection 2016.


PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

MeSH terms

  • Algorithms
  • Decision Support Systems, Clinical*
  • Decision Support Techniques
  • Feasibility Studies
  • Female
  • Humans
  • Male
  • Risk Factors
  • Stress Disorders, Post-Traumatic*
  • Support Vector Machine*