AIMC Topic: Patient Admission
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Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission.
Scientific reports
Jul 1, 2025
Scientists aim to create a system that can predict the likelihood of newborns being admitted to the neonatal intensive care unit (NICU) by combining various statistical methods. This prediction could potentially reduce the negative health outcomes, d...
Identifying real time surveillance indicators to estimate COVID-19 hospital admissions in Colorado during and after the public health emergency.
Scientific reports
Jul 1, 2025
Questions remain about how best to focus surveillance efforts for COVID-19 and other emerging respiratory diseases. We used an archive of COVID-19 data in Colorado from October 2020 to March 2024 to reconstruct seven real-time surveillance indicators...
Prediction of postoperative intensive care unit admission with artificial intelligence models in non-small cell lung carcinoma.
European journal of medical research
Apr 15, 2025
BACKGROUND: There is no standard practice for intensive care admission after non-small cell lung cancer surgery. In this study, we aimed to determine the need for intensive care admission after non-small cell lung cancer surgery with deep learning mo...
Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.
PloS one
Feb 18, 2025
OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the intensive care unit (ICU).
Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis.
Cardiovascular diabetology
Jan 8, 2025
Chronic heart failure (CHF) poses a significant threat to human health. The stress hyperglycemia ratio (SHR) is a novel metric for accurately assessing stress hyperglycemia, which has been correlated with adverse outcomes in various major diseases. H...
Chronic Disease
Predictive Value of Tests
Cause of Death
Heart Failure
Patient Admission
Risk Assessment
Blood Glucose
Time Factors
Hyperglycemia
Prognosis
Retrospective Studies
Intensive Care Units
Female
Risk Factors
Aged
Aged, 80 and over
Male
Middle Aged
Humans
Machine Learning
Decision Support Techniques
Databases, Factual
Biomarkers
Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.
BMC emergency medicine
Jan 6, 2025
INTRODUCTION: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the...
Early prediction of intensive care unit admission in emergency department patients using machine learning.
Australian critical care : official journal of the Confederation of Australian Critical Care Nurses
Dec 5, 2024
BACKGROUND: The timely identification and transfer of critically ill patients from the emergency department (ED) to the intensive care unit (ICU) is important for patient care and ED workflow practices.
Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.
BMJ health & care informatics
Dec 4, 2024
OBJECTIVES: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe...
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...
Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.
European respiratory review : an official journal of the European Respiratory Society
Nov 13, 2024
BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.
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