Predicting Probable Persistent PTSD Following the Sewol Ferry Disaster: Development of an AI Algorithm Based on Psychological Assessments and Biological Markers.

OBJECTIVE

Prolonged post-traumatic stress disorder (PTSD) carries substantial personal and public-health costs, yet early identification of individuals at risk remains difficult. This study aimed to develop a novel artificial intelligence (AI)-based predictive algorithm using psychological, biological, and psychosocial data collected at initial assessment to enhance early identification of individuals at heightened risk for probable persistent PTSD.

METHODS

This study included 88 bereaved family members of the victims of the Sewol ferry disaster, divided into probable persistent PTSD (n=67) and probable remitted (n=21) groups based on 4-year follow-up assessments. Demographic, blood test, and psychological data were collected during initial evaluations. Models compared linear discriminant analysis (LDA) with tree-based learners under stratified cross-validation; class imbalance was addressed with Borderline Synthetic Minority Oversampling Technique, and recursive feature elimination identified parsimonious predictors.

RESULTS

The LDA model demonstrated the highest performance, with an area under the receiver operating characteristic curve of 0.858. Psychosocial features dominated prediction: higher anxiety, depression, insomnia, and intrusive rumination increased risk, whereas greater positive resources and functional social support were protective. Routine physiological markers showed limited incremental value.

CONCLUSION

Findings support a practical intake pathway in which brief psychosocial measures are used with an interpretable classifier to triage high-risk individuals to targeted interventions. External validation, calibration, decision-curve analysis, and broader biomarker panels are needed to confirm transportability and optimize clinical utility.