AI Medical Compendium Topic

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Emergency Service, Hospital

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Care Models for Acute Chest Pain That Improve Outcomes and Efficiency: JACC State-of-the-Art Review.

Journal of the American College of Cardiology
Existing assessment pathways for acute chest pain are often resource-intensive, prolonged, and expensive. In this review, the authors describe existing chest pain pathways and current issues at the patient and system level, and provide an overview of...

Comparative analysis of machine learning approaches for predicting frequent emergency department visits.

Health informatics journal
BACKGROUND: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users.

Recent Updates and Technological Developments in Evaluating Cardiac Syncope in the Emergency Department.

Current cardiology reviews
Syncope is a commonly encountered problem in the emergency department (ED), accounting for approximately 3% of presenting complaints. Clinical assessment of syncope can be challenging due to the diverse range of conditions that can precipitate the sy...

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

Pediatric emergency care
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...

Machine learning for outcome predictions of patients with trauma during emergency department care.

BMJ health & care informatics
OBJECTIVES: To develop and evaluate a machine learning model for predicting patient with trauma mortality within the US emergency departments.

Predictive Risk Models for Wound Infection-Related Hospitalization or ED Visits in Home Health Care Using Machine-Learning Algorithms.

Advances in skin & wound care
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...

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
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
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 ...