Beyond clinical variables: Machine learning integration of clinical and contextual factors for predicting heart failure readmissions

Predicting 30-day readmission after heart failure hospitalization using interpretable machine learning: evidence from a 20-year population-based rural registry.

Machine learning prediction of 30-day mortality in coronary artery disease: a retrospective multicenter study using electronic health records.

The use of a structured machine learning approach to predict 30-day mortality in patients with acute and chronic CAD demonstrated promising discriminative performance, providing valuable insights that could enhance personalized care and inform clinical decisions.

Interpretable Machine Learning for In-Hospital Mortality Prediction in Patients With Diabetes and ARDS: Development and External Validation.

BACKGROUND Patients with diabetes mellitus complicated by acute respiratory distress syndrome (ARDS) are a high-risk subgroup, but population-specific models for in-hospital mortality remain limited. We aimed to develop and externally validate machine learning models using the 2023 New Global Definition of ARDS. METH...

An Interpretable Machine Learning Framework with Clinical Nomogram for Predicting In-Hospital Mortality in Acute Ischemic Stroke Using High-Granularity Bedside Data.

BACKGROUND This multicenter study developed and validated an interpretable machine learning model integrating granular nursing and emergency department data collected within the first 24 hours to predict in-hospital mortality in acute ischemic stroke (AIS). METHODS We analyzed a retrospective cohort of 5,014 adult AI...

A Comparative Analysis of Machine Learning Models for Heart Disease Prediction Using Clinical Parameters

Cardiovascular disease remains one of the leading causes of mortality worldwide, with early detection being crucial for improving patient outcomes. This study aims to develop and validate a machine learning-based prediction model for heart disease using electronic health records from a major hospital system in New Jers...

PREDICTING HEART DISEASE RISK FROM CLINICAL VARIABLES: A GENDER-SPECIFIC MACHINE LEARNING ANALYSIS AMONG HIGH-CHOLESTEROL PATIENTS

Male sex was a statistically significant independent predictor of heart disease after controlling for other clinical variables and the findings support sex-specific screening and preventive strategies for high-cholesterol male patients and demonstrate the value of interpretable machine learning models for clinical deci...