In the past decade, two themes have emerged across suicide research. First, according to
meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker …

Antidepressant selection is largely a trial-and-error process. We used electronic health
record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants …

Suicide is a significant public health concern, and there is growing interest in using machine
learning techniques to identify people who are at a high risk of committing suicide. In this …

Non-accidental trauma (NAT) is deadly and difficult to predict. Transformer models
pretrained on large datasets have recently produced state of the art performance on diverse …

Both the quality and utility of youth suicide research depend on how we assess our
outcomes of interest: suicidal thoughts and behaviors (STBs). We now have access to more …

This study aimed to use machine learning (ML) models to predict the risk of self-harm and
suicide attempts in adolescents. We conducted secondary analysis of cross-sectional data …

Suicide is a major public health issue and a leading cause of death among children and
young people (CYP) worldwide. There is strong evidence linking adverse childhood …

Growing evidence has shown that applying machine learning models to large clinical data
sources may exceed clinician performance in suicide risk stratification. However, many …

Objective Preventing suicide in US youth is of paramount concern, with rates increasing over
50% between 2007 and 2018. Statistical modeling using electronic health records may help …

Artificial Intelligence (AI) technologies have been applied in various domains, including
early childhood education (ECE). Integration of AI educational technology is a recent …