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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...

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Detalles Bibliográficos
Autores principales: Lee, Sangil, Mohr, Nicholas M., Street, W. Nicholas, Nadkarni, Prakash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Department of Emergency Medicine, University of California, Irvine School of Medicine 2019
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
Descripción
Sumario: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.