AIMC Topic: Machine Learning

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Improving the Accuracy of a Wearable Uroflowmeter for Incontinence Monitoring Under Dynamic Conditions: Leveraging Machine Learning Methods.

Biosensors
Urinary incontinence affects many women, yet there are no monitoring devices capable of accurately capturing flow dynamics during everyday activities. Building on our initial development of a wearable personal uroflowmeter, this study enhances the de...

Naturalistic acute pain states decoded from neural and facial dynamics.

Nature communications
Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and f...

Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation.

Journal of cancer research and clinical oncology
PURPOSE: This study aims to develop an effective machine learning (ML)-based predictive model for the recurrence of borderline ovarian tumor (BOT), and provide the guidelines of accurate clinical diagnosis and precise treatment for patients.

Predictive athlete performance modeling with machine learning and biometric data integration.

Scientific reports
The Purpose of this study is to propose a new integrative framework for athletic performance prediction based on state-of-the-art machine learning analysis and biometric data biometric scanning. By merging physiological signals i.e., Heart rate varia...

[Progress in method development and application of distributed learning for estimation of epidemiological effect].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
To systematically review the progress in the method development and application of distributed learning in the estimation of epidemiological effect and provide methodological reference for multi-center studies. We conducted a literature retrieval f...

Investigating the Use of Electrooculography Sensors to Detect Stress During Working Activities.

Sensors (Basel, Switzerland)
To tackle work-related stress in the evolving landscape of Industry 5.0, organizations need to prioritize employee well-being through a comprehensive strategy. While electrocardiograms (ECGs) and electrodermal activity (EDA) are widely adopted physio...

Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic-Ischemic Encephalopathy Injury.

Sensors (Basel, Switzerland)
Hypoxic-Ischemic Encephalopathy (HIE) occurs in patients who experience a decreased flow of blood and oxygen to the brain, with the optimal window for effective treatment being within the first six hours of life. This puts a significant demand on med...

Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study.

BMC oral health
BACKGROUND: Oral mucosal lesions are widespread globally, have a high prevalence in clinical practice, and significantly impact patients' quality of life. However, their pathogenesis remains unclear. Recent evidences suggested that hematological para...

Machine learning for the prediction of diabetes-related amputation: a systematic review and meta-analysis of diagnostic test accuracy.

Clinical and experimental medicine
Although machine learning is frequently used in medicine for predictive purposes, its accuracy in diabetes-related amputation (DRA) remains unclear. From establishing the database until December 2024, we conducted a comprehensive search of PubMed, We...