AIMC Topic: Length of Stay
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Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.
BMJ health & care informatics
Jun 26, 2025
OBJECTIVE: Machine learning (ML) models have emerged as tools to predict length of stay (LOS) and disposition decision (DD) in emergency departments (EDs) to combat overcrowding. However, site-specific ML models are not transferable to different site...
The extent of Skeletal muscle wasting in prolonged critical illness and its association with survival: insights from a retrospective single-center study.
BMC anesthesiology
May 26, 2025
OBJECTIVE: Muscle wasting in critically ill patients, particularly those with prolonged hospitalization, poses a significant challenge to recovery and long-term outcomes. The aim of this study was to characterize long-term muscle wasting trajectories...
Using interpretable survival analysis to assess hospital length of stay.
BMC health services research
May 22, 2025
Accurate in-hospital length of stay prediction is a vital quality metric for hospital leaders and health policy decision-makers. It assists with decision-making and informs hospital operations involving factors such as patient flow, elective cases, a...
Influence of the CONCERN Early Warning System on Unanticipated ICU Transfers, In-Hospital Mortality, and Length of Stay: Results from a Multi-site Pragmatic Randomized Controlled Clinical Trial.
AMIA ... Annual Symposium proceedings. AMIA Symposium
May 22, 2025
Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surveillance documentation patterns to predict deterioration risks for hospitalized patients. In a retros...
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...
Clinical assessment of the criticality index - dynamic, a machine learning prediction model of future care needs in pediatric inpatients.
PloS one
Apr 30, 2025
OBJECTIVE: To assess patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations (ICU vs. non-ICU) by the Criticality Index-Dynamic (CI-D), with the goal of enhancing the CI-D.
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 ...
Predicting prolonged length of in-hospital stay in patients with non-ST elevation myocardial infarction (NSTEMI) using artificial intelligence.
International journal of cardiology
Apr 11, 2025
BACKGROUND: Patients presenting with non-ST elevation myocardial infarction (NSTEMI) are typically evaluated using coronary angiography and managed through coronary revascularization. Numerous studies have demonstrated the benefits of expedited disch...
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...
A comparative study of neuro-fuzzy and neural network models in predicting length of stay in university hospital.
BMC health services research
Mar 27, 2025
BACKGROUND: The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefo...
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