Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec.

ABSTRACT: Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to

SUBMITTER: Gholi Zadeh Kharrat F 

PROVIDER: S-EPMC10990247 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature