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Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview
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 practic...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Department of Emergency Medicine, University of California, Irvine School of Medicine
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6404711/ https://www.ncbi.nlm.nih.gov/pubmed/30881539 http://dx.doi.org/10.5811/westjem.2019.1.41244 |
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author | Lee, Sangil Mohr, Nicholas M. Street, W. Nicholas Nadkarni, Prakash |
author_facet | Lee, Sangil Mohr, Nicholas M. Street, W. Nicholas Nadkarni, Prakash |
author_sort | Lee, Sangil |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6404711 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Department of Emergency Medicine, University of California, Irvine School of Medicine |
record_format | MEDLINE/PubMed |
spelling | pubmed-64047112019-03-15 Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview Lee, Sangil Mohr, Nicholas M. Street, W. Nicholas Nadkarni, Prakash West J Emerg Med Emergency Department Operations 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. Department of Emergency Medicine, University of California, Irvine School of Medicine 2019-03 2019-02-14 /pmc/articles/PMC6404711/ /pubmed/30881539 http://dx.doi.org/10.5811/westjem.2019.1.41244 Text en Copyright: © 2019 Lee et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) License. See: http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Emergency Department Operations Lee, Sangil Mohr, Nicholas M. Street, W. Nicholas Nadkarni, Prakash Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview |
title | Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview |
title_full | Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview |
title_fullStr | Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview |
title_full_unstemmed | Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview |
title_short | Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview |
title_sort | machine learning in relation to emergency medicine clinical and operational scenarios: an overview |
topic | Emergency Department Operations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6404711/ https://www.ncbi.nlm.nih.gov/pubmed/30881539 http://dx.doi.org/10.5811/westjem.2019.1.41244 |
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