AIMC Topic: Patient Readmission
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Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review.
BMJ open
Jun 25, 2025
BACKGROUND: Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care. In cardiology, ML plays a crucial role in risk prediction and patient stratificati...
Predicting 14-day readmission in middle-aged and elderly patients with pneumonia using emergency department data: a multicentre retrospective cohort study with a survival machine learning approach.
BMJ open
Jun 17, 2025
OBJECTIVES: Unplanned pneumonia readmissions increase patient morbidity, mortality and healthcare costs. Among pneumonia patients, the middle-aged and elderly (≥45 years old) have a significantly higher risk of readmission compared with the young. Gi...
Strategies to Reduce Hospital Length of Stay: Evidence and Challenges.
Medicina (Kaunas, Lithuania)
May 20, 2025
Hospital length of stay (HLOS) is a critical healthcare metric influencing patient outcomes, resource utilization, and healthcare costs. While reducing HLOS can improve hospital efficiency and patient throughput, it also poses risks such as premature...
Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation.
BMC nephrology
Apr 22, 2025
BACKGROUND: Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learning approaches offer promising solutions for risk prediction, their clinical application often lacks ...
Artificial intelligence prediction model for readmission after DIEP flap breast reconstruction based on NSQIP data.
Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Apr 20, 2025
BACKGROUND: Readmissions following deep inferior epigastric perforator (DIEP) flap breast reconstruction represent a significant healthcare burden, yet current risk prediction methods lack precision in identifying high-risk patients. We developed a m...
Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.
Cancer control : journal of the Moffitt Cancer Center
Apr 17, 2025
BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can lead to cardiovascular-related unplanned readmissions.ObjectiveWe aimed to develop a machine learnin...
Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.
BMC cardiovascular disorders
Apr 7, 2025
BACKGROUND: Heart failure (HF) impacts nearly 6 million individuals in the U.S., with a projected 46% increase by 2030, is creating significant healthcare burdens. Predictive models, particularly machine learning (ML)-based models, offer promising so...
Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.
Nature medicine
Apr 2, 2025
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pr...
Explainable machine learning to identify risk factors for unplanned hospital readmissions in Nova Scotian hospitals.
Computers in biology and medicine
Mar 26, 2025
OBJECTIVE: A report from the Canadian Institute for Health Information found unplanned hospital readmissions (UHR) common, costly, and potentially avoidable, estimating a $1.8 billion cost to the Canadian healthcare system associated with inpatient r...
Using Natural Language Processing in the LACE Index Scoring Tool to Predict Unplanned Trauma and Surgical Readmissions in South Africa.
World journal of surgery
Mar 9, 2025
BACKGROUND: Unplanned and potentially avoidable readmission within 30 days post discharge is a major financial burden.
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