Fig. 3.

Random forest prediction models can predict future self-harm events with baseline information, clinical status of last month, and instability of clinical variables. Using sliding windows of 6 or 12 months, we examined the feasibilities of predicting future self-harm events. On average, our models can achieve a reasonable performance with AUROC of 0.65 regarding window sizes. Particularly, the performance was better when using prior 6-month information to predict future 6-month self-harm events between month 11 and 23 with a AUROC = 0.70 ± 0.071 (Panel A).