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.

Figure 1

Diagram of artificial neuron networks.

Figure 2

Diagram of decision tree. The original figure was created by Ramezankhani et al.; the link is https://bmjopen.bmj.com/content/6/12/e013336.long. The shading and formatting of the lines between tree nodes and the text font are modified. FPG, Fasting Plasma Glucose; PCPG, Post Challenge Plasma Glucose.

Figure 3

K-fold cross validation.* *The datasets are divided into several, equally sized subsets. The model is trained on subsets (training sets). After the training process, the model is tested on the remaining subsets (test sets). According to the number of subsets partitioned, user tests k-fold cross-validation. In ten-fold cross-validation, for example, one may use 10 results of 10-fold cross-validation.