Background: Mental health treatment is hindered by the limited number of mental health care providers and the infrequency of care. Digital mental health technology can help supplement treatment by remotely monitoring patient symptoms and predicting mental health crises in between clinical visits. However, the feasibility of digital mental health technologies has not yet been sufficiently explored.
View Article and Find Full Text PDFPurpose Of Review: This review aims to evaluate the current psychiatric applications and limitations of machine learning (ML), defined as techniques used to train algorithms to improve performance at a task based on data. The review emphasizes the clinician's role in ensuring equitable and effective patient care and seeks to inform mental health providers about the importance of clinician involvement in these technologies.
Recent Findings: ML in psychiatry has advanced through electronic health record integration, disease phenotyping, and remote monitoring through mobile applications.
Objective: Caregiving to a sick or disabled relative is a key chronic stress model in health psychology. However, caregiving is not uniformly stressful, and this study tested whether caregiving effects on life satisfaction and allostatic load varies by caring intensity, i.e.
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