Latest AI and machine learning research in depression for healthcare professionals.
Depression and anxiety are prevalent mental health disorders that frequently cooccur, with anxiety significantly influencing both the manifestation and treatment of depression. An accurate assessment of anxiety levels in individuals with depression is crucial to develop effective and personalized treatment plans. This study proposes a new noninvasive method for quantifying anxiety severity by an...
The adoption of EHRs has expanded opportunities to leverage data-driven algorithms in clinical care and research. A major bottleneck in effectively conducting multi-institutional EHR studies is the data heterogeneity across systems with numerous codes that either do not exist or represent different clinical concepts across institutions. The need for data privacy further limits the feasibility of...
Prior efforts in building computer-assisted pronunciation training (CAPT) systems often treat automatic pronunciation assessment (APA) and mispronun...
This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depres...
Depression is one of the leading causes of disability worldwide, posing a severe burden on individuals, healthcare systems, and society at large. Re...
Individual's general well-being is greatly impacted by mental health conditions including depression and Post-Traumatic Stress Disorder (PTSD), unde...
This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic character...
Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely...
More and more people are experiencing pressure from work, life, and education. These pressures often lead to an anxious state of mind, or even the e...
Feature selection is an important but challenging task in causal inference for obtaining unbiased estimates of causal quantities. Properly selected ...
Negative emotions such as loneliness, depression, and anxiety (LDA) are prevalent and pose significant challenges to emotional well-being. Traditional...
BACKGROUND: Literature on how to translate information extracted from clinical progress notes into numeric scores for 3-step theory of suicide (3ST) f...
OBJECTIVE: Current clinical evaluation may tend to lack precision in detecting depression in Parkinson's disease (DPD). Radiomics features have gradua...
INTRODUCTION: Dissociation is associated with clinical severity, increased risk of suicide and self-harm, and disproportionately affects adolescents a...
This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and...
This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We ...
Traditional depression screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical li...
Recently, multimodal depression recognition for clinical interviews (MDRC) has recently attracted considerable attention. Existing MDRC studies main...
Stigma has emerged as one of the major obstacles to effectively diagnosing depression, as it prevents users from open conversations about their stru...
The vast repositories of Electronic Health Records (EHR) and medical claims hold untapped potential for studying rare but critical events, such as s...