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

We described two important health informatics-related topics that are relevant to emergency care and research: machine learning and natural language processing (NLP). Traditionally, the machine-learning model in healthcare has suffered from low external validity or poor portability between sites, but this seems to be changing with active employment of creative solutions. NLP is highly problem-specific, and the tools available are intended for use by programmers rather than end-users, except for speech recognition and machine translation (the use of software to translate text or speech from one language to another). NLP is being used more for research purposes, but there is no general purpose information-extraction tool because what one chooses to extract depends on the problem one is trying to solve. Computational artifacts are complex and hinder our ability to predict the performance of these tools. It is important to carefully evaluate these tools using both subjective and objective approaches. It is prime time for clinicians and researchers in emergency medicine to take full advantage of health informatics to improve patient care.

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