Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.
Journal:
PLoS medicine
Published Date:
Nov 1, 2018
Abstract
BACKGROUND: Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one.
Authors
Keywords
Adolescent
Adult
Age Factors
Aged
Aged, 80 and over
Data Mining
Electronic Health Records
Emergency Service, Hospital
England
Female
Health Services Needs and Demand
Health Status
Humans
Machine Learning
Male
Middle Aged
Needs Assessment
Patient Admission
Reproducibility of Results
Risk Assessment
Risk Factors
Sex Factors
Socioeconomic Factors
Time Factors
Young Adult