A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a patient's risk of hepatocellular carcinoma (HCC), the most common ...

Long-term lithium therapy remains the most effective maintenance treatment for bipolar disorder, yet it poses a significant ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...

This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates Random Forest ...

Diabetes affects over 537 million adults globally, with early detection critical for effective treatment and management. This project develops a machine learning classification model to predict ...

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...

It’s everywhere, as the author learned the hard way while making as little contact as possible with machine learning and generative artificial intelligence. It’s everywhere, as the author learned the ...

Introduction Frailty is a common condition in older adults with diabetes, which significantly increases the risk of adverse health outcomes. Early identification of frailty in this population is ...

The final, formatted version of the article will be published soon. Background): Diabetes Mellitus (DM) is a chronic metabolic disorder that poses a significant global health challenge, affecting ...

Postpartum depression (PPD) is a common and serious mental health complication after childbirth, with potential negative consequences for both the mother and her infant. This study aimed to develop an ...