AIMC Topic: Machine Learning
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Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease.
Pediatric rheumatology online journal
Jun 2, 2025
BACKGROUND: Kawasaki disease (KD), a pediatric systemic vasculitis, lacks reliable diagnostic biomarkers and exhibits immune heterogeneity, complicating clinical management. Current therapies face challenges in targeting specific immune pathways and ...
Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.
Journal of neuroengineering and rehabilitation
Jun 2, 2025
BACKGROUND: Classifying and predicting Parkinson's disease (PD) is challenging because of its diverse subtypes based on severity levels. Currently, identifying objective biomarkers associated with disease severity that can distinguish PD subtypes in ...
Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation.
BMC cardiovascular disorders
Jun 2, 2025
OBJECTIVE: Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (AF). This study aimed to evaluate the incidence and predictors of LAT in patients with paroxysmal AF...
Evaluating the Impact of AI-Based Model-Informed Drug Development (MIDD): A Comparative Review.
The AAPS journal
Jun 2, 2025
Model-informed drug development (MIDD) methods play critical role to ensure development of efficacious, and safe individualized therapies. The application of artificial intelligence/machine learning (AI/ML) within the field of drug development has ex...
Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks.
Scientific reports
Jun 2, 2025
The application of Machine Learning has become a revolutionary instrument in the domain of pharmaceutical research. Machine learning enables the modelling of Quantitative Structure Property Relationship, a crucial task in forecasting the physiochemic...
Out-of-distribution reject option method for dataset shift problem in early disease onset prediction.
Scientific reports
Jun 2, 2025
Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for use is often hindered by dataset shift, which refers to discrepancies in data distribution between th...
Machine learning-powered activatable NIR-II fluorescent nanosensor for in vivo monitoring of plant stress responses.
Nature communications
Jun 2, 2025
Real-time monitoring of plant stress signaling molecules is crucial for early disease diagnosis and prevention. However, existing methods are often invasive and lack sensitivity, rendering them inadequate for continuous monitoring of subtle plant str...
Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development.
JMIR medical informatics
Jun 2, 2025
BACKGROUND: Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting appropriate medications.
Predicting the tensile properties of heat treated and non-heat treated LPBFed AlSi10Mg alloy using machine learning regression algorithms.
PloS one
Jun 2, 2025
In this study, the ability of machine learning algorithms to predict tensile properties of both heat-treated and non-heat treated LPBFed AlSi10Mg alloy is investigated. The data was analyzed using various Machine Learning Regression (MLR) models such...
Early detection of occupational stress: Enhancing workplace safety with machine learning and large language models.
PloS one
Jun 2, 2025
Occupational stress is a major concern for employers and organizations as it compromises decision-making and overall safety of workers. Studies indicate that work-stress contributes to severe mental strain, increased accident rates, and in extreme ca...
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