AIMC Topic: Machine Learning
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A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection.
Proceedings of the National Academy of Sciences of the United States of America
Jun 6, 2025
Accurate identification of sleep stages and disorders is crucial for maintaining health, preventing chronic conditions, and improving diagnosis and treatment. Direct respiratory measurements, as key biomarkers, are missing in traditional wrist- or fi...
Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.
BMC medical informatics and decision making
Jun 6, 2025
The rapid decline of kidney function in middle-aged and elderly people has become an increasingly serious public health problem. Machine learning (ML) technology has substantial potential to disease prediction. The present study use dataset from the ...
Integrated network toxicology, machine learning and molecular docking reveal the mechanism of benzopyrene-induced periodontitis.
BMC pharmacology & toxicology
Jun 6, 2025
BACKGROUND: Environmental pollutants, particularly from air pollution and tobacco smoke, have emerged as significant risk factors. Benzopyrene (BaP), a Group 1 carcinogen, is ubiquitously present in these pollutants, yet its molecular mechanisms in p...
AI for mental health: clinician expectations and priorities in computational psychiatry.
BMC psychiatry
Jun 6, 2025
Mental disorders represent a major global health challenge, with an estimated lifetime prevalence approaching 30%. Despite the availability of effective treatments, access to mental health care remains inadequate. Computational psychiatry, leveraging...
Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary neuroendocrine tumors and craniopharyngioma.
Scientific reports
Jun 6, 2025
Posterior pituitary tumors (PPTs) are rare neoplasms, but easily misdiagnosed as pituitary neuroendocrine tumor (PitNET) and craniopharyngioma. This study aimed to differentiate PPTs from PitNET and craniopharyngioma using a machine learning method b...
The value of intratumoral and peritumoral ultrasound radiomics model constructed using multiple machine learning algorithms for non-mass breast cancer.
Scientific reports
Jun 6, 2025
To investigate the diagnostic capability of multiple machine learning algorithms combined with intratumoral and peritumoral ultrasound radiomics models for non-massive breast cancer in dense breast backgrounds. Manual segmentation of ultrasound image...
Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome.
Scientific reports
Jun 6, 2025
Incomplete tetraplegia, incomplete paraplegia, and cauda equina syndrome are major neurological disorders that significantly reduce patients' quality of life, primarily due to impaired motor function and gait instability. Although conventional neurol...
Predicting opportunities for improvement in trauma care using machine learning: a retrospective registry-based study at a major trauma centre.
BMJ open
Jun 6, 2025
OBJECTIVE: To develop models to predict opportunities for improvement in trauma care and compare the performance of these models to the currently used audit filters.
Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.
BMC microbiology
Jun 6, 2025
Gut microbiota has been implicated in the pathogenesis of multiple gastrointestinal (GI) and systemic metabolic and inflammatory disorders where disrupted gut microbiota composition and function (dysbiosis) has been found in multiple studies. Thus, h...
Emotion recognition with multiple physiological parameters based on ensemble learning.
Scientific reports
Jun 6, 2025
Emotion recognition is a key research area in artificial intelligence, playing a critical role in enhancing human-computer interaction and optimizing user experience design. This study explores the application and effectiveness of ensemble learning m...
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