AIMC Topic: Patient Readmission

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Machine learning-driven prediction of readmission risk in heart failure patients with diabetes: synergistic assessment of inflammatory and metabolic biomarkers.

International journal of cardiology
BACKGROUND: Heart failure (HF) and diabetes mellitus (DM) frequently coexist, exacerbating disease progression and increasing hospital readmission risk. Accurate prediction of readmission in HF patients with DM remains a clinical challenge. This stud...

Machine learning approaches for predicting heart failure readmissions.

Postgraduate medical journal
PURPOSE: This study aims to develop and evaluate machine learning (ML) models to predict the likelihood of hospital readmission within 30 days after discharge for patients with heart failure (HF). The goal is to compare the predictive accuracy of ML ...

The "Outpatient Arthroplasty Risk Assessment" Score for Same Day Outpatient Primary Total Joint Arthroplasty: A Multicenter Study.

The Journal of arthroplasty
BACKGROUND: The Outpatient Arthroplasty Risk Assessment (OARA) Score was developed to risk-stratify patients for safe same-day discharge outpatient total joint arthroplasty (TJA). It has demonstrated predictive ability for length of stay in primary T...

Insights From Inputs: Enhancing Revision Total Joint Arthroplasty Resource Allocation With Machine Learning Prediction.

The Journal of arthroplasty
BACKGROUND: Revision total knee arthroplasty (rTKA) and revision total hip arthroplasty (rTHA) are among the most resource-intensive orthopaedic procedures. The primary aim of this study was to compare the accuracy of machine learning models between ...

Development of Multivariable Prediction Models for 30-Day Risk of Readmission After COPD Hospital Admission: A Retrospective Cohort Study Using Electronic Medical Record Data from 7 Hospitals.

Studies in health technology and informatics
BACKGROUND: Approximately 20% of patients who are discharged from hospital for an acute exacerbation of COPD (AECOPD) are readmitted within 30 days. Prediction scores are helpful to identify those who are at higher risk of readmission, such that they...

AI Bias and Confounding Risk in Health Feature Engineering for Machine Learning Classification Task.

Studies in health technology and informatics
Recent advancements in machine learning bring unique opportunities in health fields but also pose considerable challenges. Due to stringent ethical considerations and resource constraints, health data can vary in scope, population coverage, and colle...

A comprehensive review of ICU readmission prediction models: From statistical methods to deep learning approaches.

Artificial intelligence in medicine
The prediction of Intensive Care Unit (ICU) readmission has become a crucial area of research due to the increasing demand for ICU resources and the need to provide timely interventions to critically ill patients. In recent years, several studies hav...

A Meta-Analysis of the Diagnostic Test Accuracy of Artificial Intelligence for Predicting Emergency Department Revisits.

Journal of medical systems
The revisit of the emergency department (ED) is a key indicator of emergency care quality. Various strategies have been proposed to reduce ED revisits, including the use of artificial intelligence (AI) models for prediction. However, AI model perform...

New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance.

Scientific reports
Transitional care may play a vital role in the sustainability of Europe's future healthcare system, offering solutions for relocating patient care from hospital to home, therefore addressing the growing demand for medical care as the population is ag...

Machine Learning-Based Hospital Readmission Prediction: A Comparative Analysis of Speciality-Specific vs. All-Specialities Models.

Studies in health technology and informatics
Hospital readmissions are a major challenge for healthcare systems, leading to increased costs and adverse patient outcomes. Predicting which patients are at risk of readmission is critical for improving care and optimizing resource allocation. This ...