AIMC Topic: Depression
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Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review.
Psychiatry research
Nov 19, 2024
The youth mental health crisis is exacerbated by limited access to care and resources. Mobile health (mHealth) platforms using predictive artificial intelligence (AI) can improve access and reduce barriers, enabling real-time responses and precision ...
Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients.
Scientific reports
Nov 19, 2024
Knee osteoarthritis (KOA) combined with depressive symptoms is prevalent and leads to poor outcomes and significant financial burdens. However, practical tools for identifying at-risk patients remain limited. A robust prediction model is needed to ad...
Predicting frailty in older patients with chronic pain using explainable machine learning: A cross-sectional study.
Geriatric nursing (New York, N.Y.)
Nov 8, 2024
Frailty is common among older adults with chronic pain, and early identification is crucial in preventing adverse outcomes like falls, disability, and dementia. However, effective tools for identifying frailty in this population remain limited. This ...
Academic-related stressors predict depressive symptoms in graduate students: A machine learning study.
Behavioural brain research
Nov 7, 2024
BACKGROUND: Graduate students face higher depression rates worldwide, which were further exacerbated during the COVID-19 pandemic. This study employed a machine learning approach to predict depressive symptoms using academic-related stressors.
Depression diagnosis: EEG-based cognitive biomarkers and machine learning.
Behavioural brain research
Nov 6, 2024
Depression is a complex mental illness that has significant effects on people as well as society. The traditional techniques for the diagnosis of depression, along with the potential of nascent biomarkers especially EEG-based biomarkers, are studied....
Detecting the impact of diagnostic procedures in Pap-positive women on anxiety using artificial neural networks.
PloS one
Oct 31, 2024
INTRODUCTION: Women who receive a result of an abnormal Papanicolaou (Pap) smear can fail to participate in follow up procedures, and this is often due to anxiety. This study aimed to apply artificial neural networks (ANN) in prediction of anxiety in...
Optimizing the Prediction of Depression Remission: A Longitudinal Machine Learning Approach.
American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics
Oct 29, 2024
Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can be enhanced by incorporating repeated assessments of symptom severity collected during treatment. P...
Characterization of Brain Abnormalities in Lactational Neurodevelopmental Poly I:C Rat Model of Schizophrenia and Depression Using Machine-Learning and Quantitative MRI.
Journal of magnetic resonance imaging : JMRI
Oct 28, 2024
BACKGROUND: A recent neurodevelopmental rat model, utilizing lactational exposure to polyriboinosinic-polyribocytidilic acid (Poly I:C) leads to mimics of behavioral phenotypes resembling schizophrenia-like symptoms in male offspring and depression-l...
Prediction of Suicidal Thoughts and Suicide Attempts in People Who Gamble Based on Biological-Psychological-Social Variables: A Machine Learning Study.
The Psychiatric quarterly
Oct 28, 2024
Recent research has shown that people who gamble are more likely to have suicidal thoughts and attempts compared to the general population. Despite the advancements made, no study to date has predicted suicide risk factors in people who gamble using ...
Federated learning-based prediction of depression among adolescents across multiple districts in China.
Journal of affective disorders
Oct 9, 2024
Depression in adolescents is a serious mental health condition that can affect their emotional and social well-being. Detailed understanding of depression patterns and status of depressive symptoms in adolescents could help identify early interventio...
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