Journal of Medical Internet Research

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study

Results: In the training set, LightGBM, XGBoost, and RF demonstrated the best performance among all models, with ROC-AUCs of 0.9977, 0.9311, and 0.9847, respectively. These models exhibited minimal ...

Meet the women using AI to detect hidden heart disease

Sonorus isn't the only organisation working to detect heart disease via AI sound analysis. Studies on AI-enabled stethoscopes ...

A smarter way to build vaccines: Scientists harness AI to target emerging alphaviruses

A team of scientists at The University of Texas Medical Branch (UTMB), led by Nikos Vasilakis, Ph.D., and Peter McCaffrey, MD ...
Morning Overview on MSN

Graphene nanodrums identify bacteria by sound in early lab tests

Researchers at TU Delft and partner organizations have built graphene sensors small enough to detect the vibrations of a ...
IEEE

Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Abstract: This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health ...
Open Heart

Prognostic value of cardiac fibroblast activation heterogeneity assessed by FAPI PET radiomics in hypertrophic cardiomyopathy: a proof-of-concept study

Background Hypertrophic cardiomyopathy (HCM) is associated with an increased risk of sudden cardiac death (SCD), and ...