For a machine learning prediction algorithm to be useful in clinical practice, it must be implementable within the clinical workflow, provide prediction that meaningfully informs decision-making and ...

A new scientific machine learning framework developed by Professors Horacio D. Espinosa, Sridhar Krishnaswamy, and collaborators accurately predicts and inversely designs the mechanical behavior of ...

Objective The predictors of in-hospital mortality for intensive care units (ICUs)-admitted heart failure (HF) patients remain poorly characterised. We aimed to develop and validate a prediction model ...

Researchers developed a machine learning technique to predict obesity risk by analyzing sociodemographic, lifestyle, and health factors. The study, which achieved 79% accuracy, identified significant ...

We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the ...

To solve this issue, predicting early heart disease is important. This research focuses on supervised machine learning techniques as a potential tool for heart disease prediction. This study has done ...

To promote the use of machine learning in cardiovascular medicine, this review provides a brief introduction to machine learning and reviews its notable applications in hypertension management and ...

Machine learning techniques help in turning this medical data into useful knowledge. Machine learning is used to design such decision support systems (DSS) that can learn and improve from their past ...