BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …

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 …

Depression is one of the leading causes of suicide worldwide. However, a large percentage
of cases of depression go undiagnosed and, thus, untreated. Previous studies have found …

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 …

Objective Disease comorbidity is a major challenge in healthcare affecting the patient's
quality of life and costs. AI-based prediction of comorbidities can overcome this issue by …

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in
the past, making it difficult to organize the research contributions in the present and identify …

There has been much research effort expended toward the use of Bayesian networks (BNs)
in medical decision-making. However, because of the gap between developing an accurate …

Emotions are spontaneous feelings that are accompanied by fluctuations in facial muscles,
which leads to facial expressions. Categorization of these facial expressions as one of the …

This paper focuses on providing a comprehensive literature review on the application of
machine learning algorithms in the diagnosis of anxiety disorder, treatment response, and …

Objective Prediction of Next-Day's Affect Using Multimodal Physiological and Behavioral
Data: Algorithm Development and Validation Study Objective Prediction of Next-Day's Affect …