BACKGROUND: Depression exhibits significant heterogeneity in antidepressant treatment response. This study aimed to develop an Electroencephalography (EEG)-based machine learning model integrating multidimensional features to predict selective seroto...
OBJECTIVE: The relationship between depression and obstructive sleep apnea (OSA) remains controversial. Therefore, this study aims to explore their association and utilize machine learning models to predict OSA among individuals with depression withi...
BACKGROUND: There is still no clinical biomarker to diagnose depression. Given the complexity of a multifactorial disease like depression, a single biomarker is unlikely to capture the full heterogeneity of the disease and be applicable in clinical p...
BACKGROUND: Depression is a psychological disorder characterized by altered self-referential cognition and impaired emotional expression. Traditional diagnostic methods can be costly or intrusive, while Speech-based analysis offers an accessible alte...
BACKGROUND: An increasing number of studies have shown that there is an inseparable connection between hysterectomy and occurrence of depression, and the impact on patient's mental health cannot be ignored. Therefore, this study utilized the National...
BACKGROUND: Early detection of depression is crucial for implementing interventions. Deep learning-based computer vision (CV), semantic, and acoustic analysis have enabled the automated analysis of visual and auditory signals.
This study aimed to investigate the functionality of the prefrontal cortex in patients with unipolar depression (UD) and bipolar depression (BD) using functional near-infrared spectroscopy (fNIRS) during a verbal fluency task (VFT). Additionally, it ...
BACKGROUND: Differentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression and treatment approaches. Digital phenotyping, which involves using data from smartphones or other d...
OBJECTIVE: Although patients prefer describing their problems using words, mental health interventions are commonly evaluated using rating scales. Fortunately, recent advances in natural language processing (i.e., AI-methods) yield new opportunities ...
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