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Machine Learning Techniques for Simulating Human Psychophysical Testing of Low-Resolution Phosphene Face Images in Artificial Vision.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 22, 2025
To evaluate the quality of artificial visual percepts generated by emerging methodologies, researchers often rely on labor-intensive and tedious human psychophysical experiments. These experiments necessitate repeated iterations upon any major/minor ...
Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model.
Ecotoxicology and environmental safety
Feb 22, 2025
Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been strongly associated with chronic kidney disease (CKD). An interpretable machine learning (ML) model was developed to predict CKD using data from the ...
Investigation of Trajectory Tracking Control in Hip Joints of Lower-Limb Exoskeletons Using SSA-Fuzzy PID Optimization.
Sensors (Basel, Switzerland)
Feb 22, 2025
The application of lower-limb exoskeleton robots in rehabilitation is becoming more prevalent, where the precision of control and the speed of response are essential for effective movement tracking. This study tackles the challenge of optimizing both...
Fast, smart, and adaptive: using machine learning to optimize mental health assessment and monitor change over time.
Scientific reports
Feb 22, 2025
In mental health, accurate symptom assessment and precise measurement of patient conditions are crucial for clinical decision-making and effective treatment planning. Traditional assessment methods can be burdensome, especially for vulnerable populat...
Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.
BMC medical informatics and decision making
Feb 22, 2025
BACKGROUND: Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool...
Population norms for the EQ-5D-5L for Hungary: comparison of online surveys and computer assisted personal interviews.
The European journal of health economics : HEPAC : health economics in prevention and care
Feb 21, 2025
BACKGROUND AND OBJECTIVES: The aims of this study were to provide population norms for EQ-5D-5L in Hungary and investigate the differences in EQ-5D-5L normative data by survey mode, i.e. online surveys and computer assisted personal interviews (CAPI)...
Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study.
Journal of affective disorders
Feb 21, 2025
BACKGROUND: There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but also explore whether these features have neurobiological correlates.
Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis - based on machine learning algorithms.
Hypertension research : official journal of the Japanese Society of Hypertension
Feb 21, 2025
This study aims to investigate the association between visit-to-visit blood pressure variability (VVV) in early stage of continuous ambulatory peritoneal dialysis (CAPD) and long-term clinical outcomes, utilizing machine learning algorithms. Patients...
Development and validation of short-term, medium-term, and long-term suicide attempt prediction models based on a prospective cohort in Korea.
Asian journal of psychiatry
Feb 21, 2025
BACKGROUND: This study aimed to develop and validate prediction models for short-(3 months), medium-(1 year), and long-term suicide attempts among high-risk individuals in South Korea.
Multi-feature fusion method combining brain functional connectivity and graph theory for schizophrenia classification and neuroimaging markers screening.
Journal of psychiatric research
Feb 21, 2025
BACKGROUND: The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researchers proposed machine learning algorithms by combining FC or GT to identify SZ from healthy controls. ...
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