TCTMD

AI-ECG Models Detect Structural Heart Disease, Identify At-Risk Patients

The tools may help address underdiagnosis in this area, though there is still room for improvement in screening performance.
Tech Xplore

Hybrid AI model cuts financial forecasting error across stocks and crypto

A hybrid artificial intelligence model that combines two well-established deep learning techniques has improved the accuracy ...

First AI agent and risk prediction model for precision diabetes management

A research team at The Hong Kong Polytechnic University (PolyU) has developed Hong Kong's first "AI Agent for Precision ...

Using AI to provide new insights into why Ebola outcomes differ between patients

There is currently a serious outbreak in the Democratic Republic of the Congo of the severe and often fatal illness. Symptoms ...
RMD Open

Development and assessment of an assisted diagnosis model using machine learning for identifying adult-onset Still’s disease in fever of unknown origin: a retrospective …

Background Adult-onset Still’s disease (AOSD) is a systemic autoinflammatory disorder lacking a gold-standard diagnostic ...
Journal of Medical Internet Research

Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical ...

Objective: This study aimed to identify critical time points in SA-AKI progression development and validate dynamic, stratified machine learning prediction models for moderate-to-severe (Kidney ...
Medscape

Advanced Machine Learning Model Predicts Onset of Epilepsy and Depression

A large study applies advanced machine learning to identify shared risk factors and predictors of disease onset in patients with epilepsy and depression.