AIMC Topic: Emergency Service, Hospital
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Emergency department frequent user subgroups: Development of an empirical, theory-grounded definition using population health data and machine learning.
Families, systems & health : the journal of collaborative family healthcare
Mar 1, 2021
Frequent emergency department (ED) use has been operationalized in research, clinical practice, and policy as number of visits to the ED, despite the fact that this definition lacks empirical evidence and theoretical foundation. To date, there are no...
[Overview of machine learning and its application in the management of emergency services].
Revista medica de Chile
Feb 1, 2021
The processes associated with health care generate a large amount of information that is difficult to analyze using standard statistical procedures. In this context, disciplines such as Data Science became relevant, mainly through strategies such as ...
Time for clinicians to revisit their perspectives on C-statistic.
European heart journal
Jan 1, 2021
Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database.
Medicine
Dec 4, 2020
Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in health service research and may improve the prediction of health outcomes. The objective of this work...
Application Research on Gated Recurrent Unit Deep Learning Prediction and Graded Early Warning of Emergency Department Visits Based on Meteorological Environmental Data.
Biomedical and environmental sciences : BES
Oct 20, 2020
Developing a curriculum in artificial intelligence for emergency radiology.
Emergency radiology
Aug 1, 2020
Applications of artificial intelligence in the emergency department.
Emergency radiology
Aug 1, 2020
Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.
Anesthesiology
May 1, 2020
BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of disch...
A new risk stratification score for patients with suspected cardiac chest pain in emergency departments, based on machine learning.
Chinese medical journal
Apr 5, 2020
Using applied machine learning to predict healthcare utilization based on socioeconomic determinants of care.
The American journal of managed care
Jan 1, 2020
OBJECTIVES: To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data.
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