The drug development pipeline is a costly and lengthy process. Identifying high-quality "hit" compounds-those with high potency, selectivity, and favorable metabolic properties-at the earliest stages ...
Imagine a future where Artificial Intelligence (AI) can forecast medical conditions years before any symptoms appear. What ...
A machine learning–driven web tool based on 13 standard patient metrics demonstrates strong predictive performance for MASLD, ...
A review of machine learning (ML) models developed to support the management of chronic lymphocytic leukemia (CLL) have demonstrated positive outcomes, including accurate diagnosis and improved work ...
Introduction: Type 2 diabetes mellitus (T2DM) is a globally prevalent metabolic disease, and emerging studies have revealed its strong association with calcific aortic valve disease (CAVD). Chronic ...
New research across seven global biobanks shows that the DNA driving disease onset does not determine survival; instead, lifespan-linked genes and cross-trait scores hold the real clues to prognosis.
Conclusions: We identified several ML-based models predicting clinical outcomes with good discriminatory ability in people with DFU. Due to the focus on development and internal validation of the ...
We trained and tested ML systems that predict a deterioration in nine patient-reported symptoms within 30 days after treatments for aerodigestive cancers, using internal electronic health record (EHR) ...
Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...
Abstract: Diabetes Mellitus is a complex metabolic syndrome that needs prompt and correct categorization to avoid serious complications. In this paper, a deep learning method has been proposed to ...
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