Huang et al. examined the use of AI in developing a risk assessment model to enable early and targeted intervention for individuals at high-risk of suicide. A critical challenge is the high recurrence rate among individuals who have attempted suicide previously, a problem Huang et al. hope to solve by using machine learning to identify key predictors of repeat suicide attempts. Using the data of 32,701 individuals recorded in the National Suicide Surveillance System of Taiwan during 2020, this study utilized a binary decision tree regression model to identify predictors linked to recurrent suicide attempts. Results indicated that individuals prone to suicide reattempts are more likely to be younger (15-24 age bracket), unmarried, have a high school education, suffer from psychiatric disorders and engage more with psychiatric healthcare services. The re-suicide prediction model established in this study achieved an overall recognition rate of 66.3% without requiring any biological samples, and a success rate of 57.9% at predicting re-suicide. Researchers emphasize that integrating interdisciplinary cooperation and broader data will help establish a comprehensive prevention system that reduces the negative societal impact of suicide attempts.