AIMC Topic: Hospitalization
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.
PloS one
Dec 31, 2024
BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI)...
Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform.
PloS one
Dec 30, 2024
Bed regulation within Brazil's National Health System (SUS) plays a crucial role in managing care for patients in need of hospitalization. In Rio Grande do Norte, Brazil, the RegulaRN Leitos Gerais platform was the information system developed to reg...
Association between serum endocan levels and organ failure in hospitalized patients with cirrhosis.
PloS one
Dec 26, 2024
BACKGROUND & AIMS: Acute-on-chronic liver failure is a syndrome characterized by organ failure and high short-term mortality. The lack of reliable biomarkers for the early detection of acute-on-chronic liver failure is a significant challenge. Endoth...
Exploring correlates of high psychiatric inpatient utilization in Switzerland: a descriptive and machine learning analysis.
BMC psychiatry
Dec 23, 2024
BACKGROUND: This study investigated socio-demographic, psychiatric, and psychological characteristics of patients with high versus low utilization of psychiatric inpatient services. Our objective was to better understand the utilization pattern and t...
A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.
Annals of laboratory medicine
Dec 13, 2024
BACKGROUND: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles...
Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.
Renal failure
Dec 12, 2024
BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This stud...
Application of machine learning for delirium prediction and analysis of associated factors in hospitalized COVID-19 patients: A comparative study using the Korean Multidisciplinary cohort for delirium prevention (KoMCoDe).
International journal of medical informatics
Dec 1, 2024
BACKGROUND: The incidence of delirium in hospitalized coronavirus disease 2019 (COVID-19) patients is linked to adverse health outcomes. Predicting the occurrence and risk factors of delirium is key to preventing its sudden onset.
Screening for frequent hospitalization risk among community-dwelling older adult between 2016 and 2023: machine learning-driven item selection, scoring system development, and prospective validation.
Frontiers in public health
Nov 27, 2024
BACKGROUND: Screening for frequent hospitalizations in the community can help prevent super-utilizers from growing in the inpatient population. However, the determinants of frequent hospitalizations have not been systematically examined, their operat...
Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.
Journal of medical Internet research
Nov 22, 2024
BACKGROUND: Accurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited covariates and nonstandardized data have limited reproducibility when applied to the general populati...
Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators.
Journal of the American College of Cardiology
Nov 20, 2024
BACKGROUND: Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clinical care. Machine learning approaches can potentially overcome the limitations of conventional stati...
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