Background Early detection and prediction of suicidal behaviour are key factors in suicide
control. In conjunction with recent advances in the field of artificial intelligence, there is …

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 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

The timely identification of patients who are at risk of a mental health crisis can lead to
improved outcomes and to the mitigation of burdens and costs. However, the high …

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 …

For years, the scientific community has researched monitoring approaches for the detection
of certain mental disorders and risky behaviors, like depression, eating disorders, gambling …

Background Given the additional risk of suicide-related behaviors in adolescents with
allergic rhinitis (AR), it is important to use the growing field of machine learning (ML) to …

Background Machine learning (ML) algorithms based on various clinicodemographic,
psychometric, and biographic factors have been used to predict depression, suicidal …

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 …

The adoption of electronic health records (EHR) has become universal during the past
decade, which has afforded in-depth data-based research. By learning from the large …