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

Karen‐Inge Karstoft · Isaac R. Galatzer‐Levy · Alexander Statnikov · Zhiguo Li · Arieh Y. Shalev

151 citations0/0 files storedBMC Psychiatry

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Posttraumatic Stress Disorder Research Artificial Intelligence in Healthcare Traumatic Brain Injury Research

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. METHODS: Data variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials. RESULTS: The average number of MBs per cross validation was 800. MBs' mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12-32) with 13 features present in over 75% of the sets. CONCLUSIONS: Our findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML's ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.

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References

1985Stress, appraisal, and coping 2013Stress: Appraisal and Coping 1995Posttraumatic Stress Disorder in the National Comorbidity Survey 2002Short screening scales to monitor population prevalences and trends in non-specific psychological distress 2000Meta-analysis of risk factors for posttraumatic stress disorder in trauma-exposed adults.1984Stress, Appraisal, and Coping. New York, NY: Springer;2003Predictors of posttraumatic stress disorder and symptoms in adults: A meta-analysis.1999The Posttraumatic Cognitions Inventory (PTCI): Development and validation.

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