Suicide is a leading cause of death that defies prediction and challenges prevention efforts
worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means …

Background Machine learning techniques offer promise to improve suicide risk prediction. In
the current systematic review, we aimed to review the existing literature on the application of …

Abstract Purpose of Review As rates of suicide continue to rise, there is urgent need for
innovative approaches to better understand, predict, and care for those at high risk of …

Data Mining in medicine is an emerging field of great importance to provide a prognosis and
deeper understanding of disease classification, specifically in Mental Health areas. The …

Background: Chronic wounds have been a great burden to patients and the health care
system. The popularity of the internet and smart devices, such as mobile phones and tablets …

Rapid-acting interventions for the suicide crisis have the potential to transform treatment. In
addition, recent innovations in suicide research methods may similarly expand our …

Mental disorders are highly prevalent and often remain untreated. Many limitations of
conventional face-to-face psychological interventions could potentially be overcome through …

Scientific discovery is a driving force for progress involving creative problem-solving
processes to further our understanding of the world. The process of scientific discovery has …

Brief Psychotherapy assists patients to become aware and change their behavior when
facing an immediate emotional conflict, and to implement a transformation process through …

Background: Digital phenotyping and machine learning are currently being used to augment
or even replace traditional analytic procedures in many domains, including health care …