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 ...

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

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

A new technical paper titled “Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate” was published by researchers at KTH Royal Institute of Technology and ...

Heart failure is a leading cause of hospitalization and long-term disability, with many individuals progressing from subclinical disease to overt symptoms ...

An integrated framework combining first-principles calculations and machine learning was developed to predict gas-sensing performance. Key descriptors such as adsorption energy, adsorption distance, ...

Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may ...

ADM, Evolving Systems’ big data platform, securely stores and analyzes massive telecom data, from billing to network reports, ...