Psychiatry investigation · 2026
Predicting Probable Persistent PTSD Following the Sewol Ferry Disaster: Development of an AI Algorithm Based on Psychological Assessments and Biological Markers.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Shin Daun, So Beomgi, Chae Jeong-Ho, Ham Byung-Joo, Lee So Hee
Аннотация
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.
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.