AIMC Topic: Young Adult
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MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding.
IEEE journal of biomedical and health informatics
Dec 5, 2024
OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the c...
ESSN: An Efficient Sleep Sequence Network for Automatic Sleep Staging.
IEEE journal of biomedical and health informatics
Dec 5, 2024
By modeling the temporal dependencies of sleep sequence, advanced automatic sleep staging algorithms have achieved satisfactory performance, approaching the level of medical technicians and laying the foundation for clinical assistance. However, exis...
Signed Curvature Graph Representation Learning of Brain Networks for Brain Age Estimation.
IEEE journal of biomedical and health informatics
Dec 5, 2024
Graph Neural Networks (GNNs) play a pivotal role in learning representations of brain networks for estimating brain age. However, the over-squashing impedes interactions between long-range nodes, hindering the ability of message-passing mechanism-bas...
Asymmetric Multi-Task Learning for Interpretable Gaze-Driven Grasping Action Forecasting.
IEEE journal of biomedical and health informatics
Dec 5, 2024
This work tackles the automatic prediction of grasping intention of humans observing their environment. Our target application is the assistance to people with motor disabilities and potential cognitive impairments, using assistive robotics. Our prop...
Predicting adverse pregnancy outcome in Rwanda using machine learning techniques.
PloS one
Dec 5, 2024
BACKGROUND: Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term developmental challenges. This study aimed to predict these outcomes in Rwanda using supervised machine ...
Predicting mental health treatment outcomes using latent growth mixture models and machine learning in a real-world clinical setting.
Journal of psychiatric research
Dec 4, 2024
Previous models of depression outcomes have been limited by symptom heterogeneity within populations. This study conducted a retrospective analysis using latent growth mixture models to identify heterogeneous trajectories within a clinical population...
Stress Disorders, Post-Traumatic
ROC Curve
United States
Treatment Outcome
Mental Health Services
Area Under Curve
Outcome Assessment, Health Care
Veterans
Outpatients
Machine Learning
Middle Aged
Retrospective Studies
Depression
Humans
Female
Latent Class Analysis
Reproducibility of Results
Aged, 80 and over
Patient Health Questionnaire
Adolescent
Aged
Young Adult
Adult
Male
AI-based medication adherence prediction in patients with schizophrenia and attenuated psychotic disorders.
Schizophrenia research
Dec 4, 2024
OBJECTIVE: The capacity of machine-learning algorithms to predict medication adherence was assessed using data from AiCure, a computer vision-assisted smartphone application, which records the medication ingestion event.
Use of machine learning for simplification of University Personality Inventory (UPI).
Acta psychologica
Dec 4, 2024
BACKGROUND: Rapid diagnosis of mental health problems is crucial for college students. The University Personality Inventory (UPI) is a commonly used tools for assessing mental health in college students; however, it has certain limitations. This stud...
Assessing Locomotive Syndrome Through Instrumented Five-Time Sit-to-Stand Test and Machine Learning.
Sensors (Basel, Switzerland)
Dec 3, 2024
Locomotive syndrome (LS) refers to a condition where individuals face challenges in performing activities of daily living. Early detection of such deterioration is crucial to reduce the need for nursing care. The Geriatric Locomotive Function Scale (...
Deep learning versus human assessors: forensic sex estimation from three-dimensional computed tomography scans.
Scientific reports
Dec 3, 2024
Cranial sex estimation often relies on visual assessments made by a forensic anthropologist following published standards. However, these methods are prone to human bias and may be less accurate when applied to populations other than those for which ...
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