Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

Sangil Lee; Nicholas M Mohr; W. Nicholas Street; Prakash Nadkarni

doi:10.5811/westjem.2019.1.41244

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Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

Journal article

Open access

Peer reviewed

Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview

Sangil Lee, Nicholas M Mohr, W. Nicholas Street and Prakash Nadkarni

The western journal of emergency medicine, Vol.20(2), pp.219-227

03/2019

DOI: 10.5811/westjem.2019.1.41244

PMID: 30881539

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Abstract

Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care.

Emergency Department Operations

Details

Title: Subtitle: Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview
Creators: Sangil Lee
Nicholas M Mohr
W. Nicholas Street
Prakash Nadkarni
Resource Type: Journal article
Publication Details: The western journal of emergency medicine, Vol.20(2), pp.219-227
DOI: 10.5811/westjem.2019.1.41244
PMID: 30881539
NLM abbreviation: West J Emerg Med
ISSN: 1936-900X
eISSN: 1936-9018
Publisher: Department of Emergency Medicine, University of California, Irvine School of Medicine
Language: English
Date published: 03/2019
Academic Unit: Bus Admin College; Epidemiology; Emergency Medicine; Nursing; Anesthesia; Injury Prevention Research Center; Computer Science; Business Analytics
Record Identifier: 9984025281102771

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