AIMC Topic: Hospitalization
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Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.
Pediatric emergency care
Dec 1, 2021
BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we develo...
[Artificial Intelligence in internal medicine : development of a model predicting length of stay for non-elective admissions].
Revue medicale suisse
Nov 24, 2021
Efficient management of hospitalized patients requires carefully planning each stay by taking into account patients' pathologies and hospital constraints. Therefore, the ability to accurately estimate length of stays allows for better interprofession...
Exploring Features Contributing to the Early Prediction of Sepsis Using Machine Learning.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nov 1, 2021
The increasing availability of electronic health records and administrative data and the adoption of computer-based technologies in healthcare have significantly focused on medical informatics. Sepsis is a time-critical condition with high mortality,...
Predictive Risk Models for Wound Infection-Related Hospitalization or ED Visits in Home Health Care Using Machine-Learning Algorithms.
Advances in skin & wound care
Aug 1, 2021
OBJECTIVE: Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate descriptio...
An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission: Development and validation study.
Medicine
Jul 16, 2021
BACKGROUND:: In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital m...
Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study.
Journal of the American Medical Informatics Association : JAMIA
Jun 12, 2021
OBJECTIVE: The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patien...
Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events.
Studies in health technology and informatics
May 7, 2021
BACKGROUND: Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented...
Predicting pressure injury using nursing assessment phenotypes and machine learning methods.
Journal of the American Medical Informatics Association : JAMIA
Mar 18, 2021
OBJECTIVE: Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure in...
A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions.
Journal of the American Medical Informatics Association : JAMIA
Mar 18, 2021
Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on ...
Predicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning.
Medical care
Mar 1, 2021
BACKGROUND: Clinical laboratories have traditionally used a single critical value for thrombocytopenic events. This system, however, could lead to inaccuracies and inefficiencies, causing alarm fatigue and compromised patient safety.
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