Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …

In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

High prevalence of mental illness and the need for effective mental health care, combined
with recent advances in AI, has led to an increase in explorations of how the field of machine …

Suicide is a critical issue in modern society. Early detection and prevention of suicide
attempts should be addressed to save people's life. Current suicidal ideation detection (SID) …

Recent advances in AI and machine learning (ML) promise significant transformations in the
future delivery of healthcare. Despite a surge in research and development, few works have …

Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense
psychological effects on people's minds worldwide. The global technological development …

The wide implementation of electronic health record (EHR) systems facilitates the collection
of large-scale health data from real clinical settings. Despite the significant increase in …

Abstract Language is a potential source of predictors for suicidal thoughts and behaviors
(STBs), as changes in speech characteristics, communication habits, and word choice may …

Background Despite advancements in artificial intelligence (AI) to develop prediction and
classification models, little research has been devoted to real-world translations with a user …

Background Uncertainty surrounds the ethical and legal implications of algorithmic and data-
driven technologies in the mental health context, including technologies characterized as …