The American Journal of Managed Care

Machine Learning May Enable Earlier Detection of CKD Risk Factors

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
TheServerSide

False positives vs false negatives in machine learning

Community driven content discussing all aspects of software development from DevOps to design patterns. The key difference between a false positive and a false negative is that a false positive ...
The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
2 Minute Medicine

Deep-learning models displayed expert-level accuracy in detecting epileptiform discharges on electroencephalogram (EEG) recordings

Li and colleagues developed a deep-learning model to analyze EEG recordings and detect event-level EEG spikes. 2. The model achieved high accuracy and a low false-positive rate, with only 32% of human ...
EurekAlert!

Multimodal machine learning model effective at predicting response to CDK4/6 inhibitors in HR-positive, HER2-negative breast cancer patients

SAN ANTONIO – A machine learning (ML) model incorporating both clinical and genomic factors outperformed models based solely on either clinical or genomic data in predicting which patients with ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...