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

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. [West J Emerg Med. 2019;20(2)219-227.] * † ‡ § "Natural Language Processing" [Majr], which results in nearly 2400 publications related to machine learning and natural language processing (NLP) in 2017-up from 123 in 2010. This evolving technology also influences emergency care and research by aiding emergency medicine (EM) providers in several ways. First, it helps them to identify a high-risk condition by capturing data from available records or to prevent misdiagnoses by providing decision support. 3 Second, it improves workflow efficiency by providing integrated decision aids within the electronic health record (EHR). 4 Third, providers can maintain high-quality documentation in EHR in a hightempo environment. 5 We provide synopses of two clinically important health informatics applications: machine learning and NLP. These topics are diverse, and each of them is complex. Even in researching these areas, different researchers specialize in different sub-areas of machine learning or NLP. For example,

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