While ML models are powerful tools for predicting diabetes, their lack of interpretability presents a major challenge for clinical adoption. Healthcare professionals require AI models to not only be ...
Subgroup analyses stratified patients by disease severity using SOFA scores (low ≤10, medium 11–15, high >15) and creatinine levels (low ≤3 mg/dL, medium 3–5 mg/dL, high >5 mg/dL). Multiple machine ...
Machine learning tools can improve personalised prognostication of aggressive skin cancers such as Merkel cell carcinoma, a ...
Background and Objectives: Chronic kidney disease ... The predictions for ESKD occurring within a 2-year period were better than the most experienced clinician. The work here shows that predictive ...
Objective The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML ... biomarkers of disease prediction, progression, and ...
Researchers have used AI to find adalimumab effective for iMCD, highlighting the potential of machine learning in discovering ...
A Multiple Disease Prediction System using Machine Learning is a healthcare tool designed to predict the likelihood of multiple diseases by analyzing patient data. It leverages various machine ...
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Combining concepts from statistical physics with machine learning, researchers at the University of Bayreuth have shown that ...
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