AIMC Topic: Emergency Service, Hospital
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Derivation and validation of a machine learning record linkage algorithm between emergency medical services and the emergency department.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2020
OBJECTIVE: Linking emergency medical services (EMS) electronic patient care reports (ePCRs) to emergency department (ED) records can provide clinicians access to vital information that can alter management. It can also create rich databases for resea...
Predicting emergency department orders with multilabel machine learning techniques and simulating effects on length of stay.
Journal of the American Medical Informatics Association : JAMIA
Dec 1, 2019
OBJECTIVE: Emergency departments (EDs) continue to pursue optimal patient flow without sacrificing quality of care. The speed with which a healthcare provider receives pertinent information, such as results from clinical orders, can impact flow. We s...
Designing and executing a functional exercise to test a novel informatics tool for mass casualty triage.
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2019
OBJECTIVE: The testing of informatics tools designed for use during mass casualty incidents presents a unique problem as there is no readily available population of victims or identical exposure setting. The purpose of this article is to describe the...
Machine Learning-based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
The objective of this study was to design and develop a 30-day risk of hospital readmission predictive model using machine learning techniques. The proposed risk of readmission predictive model was then validated with the two most commonly used risk ...
Training and Interpreting Machine Learning Algorithms to Evaluate Fall Risk After Emergency Department Visits.
Medical care
Jul 1, 2019
BACKGROUND: Machine learning is increasingly used for risk stratification in health care. Achieving accurate predictive models do not improve outcomes if they cannot be translated into efficacious intervention. Here we examine the potential utility o...
Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules.
JAMA pediatrics
Jul 1, 2019
IMPORTANCE: Computed tomographic (CT) scanning is the standard for the rapid diagnosis of intracranial injury, but it is costly and exposes patients to ionizing radiation. The Pediatric Emergency Care Applied Research Network (PECARN) rules for ident...
Artificial intelligence and the clinical world: a view from the front line.
The Medical journal of Australia
Apr 1, 2019
Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.
Medicine
Feb 1, 2019
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...
Hospital Mortality
Systemic Inflammatory Response Syndrome
ROC Curve
Socioeconomic Factors
Triage
Emergency Service, Hospital
Singapore
Heart Rate
Male
Middle Aged
Severity of Illness Index
Sepsis
Reproducibility of Results
Aged
Aged, 80 and over
Organ Dysfunction Scores
Humans
Machine Learning
Female
Predicting Emergency Visits and Hospital Admissions During Radiation and Chemoradiation: An Internally Validated Pretreatment Machine Learning Algorithm.
JCO clinical cancer informatics
Dec 1, 2018
PURPOSE: Patients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs....
Artificial intelligence can predict daily trauma volume and average acuity.
The journal of trauma and acute care surgery
Aug 1, 2018
BACKGROUND: The goal of this study was to integrate temporal and weather data in order to create an artificial neural network (ANN) to predict trauma volume, the number of emergent operative cases, and average daily acuity at a Level I trauma center.
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