Data Science and Machine Learning in Anesthesiology

Dongwoo Chae
2020 Korean Journal of Anesthesiology  
Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared more towards making accurate predictions. ML is a field of artificial intelligence concerned with developing algorithms and models to perform prediction tasks in the absence of explicit instructions. Most ML applications, despite being highly variable in the topics that they deal with, generally follow a common workflow. For classification tasks, a
more » ... searcher typically tests various ML models and compares the predictive performance with the reference logistic regression model. The main advantage of ML is in its ability to deal with many features with complex interactions and its specific focus on maximizing predictive performance. However, the emphasis on data-driven prediction can sometimes neglect mechanistic understanding. This article mainly focuses on supervised ML as applied to electronic health records (EHR) data. The main limitation of EHR based studies is in the difficulty of establishing causal relationships. However, low cost and rich information content provide great potential to uncover hitherto unknown correlations. In this review, the basic concepts of ML are introduced along with important terms that any ML researcher should know. Practical tips regarding the choice of software and computing devices are provided. Towards the end, several examples of successful application of ML to anesthesiology are discussed. The goal of this article is to provide a basic roadmap to novice ML researchers working in the field of anesthesiology.
doi:10.4097/kja.20124 pmid:32209960 pmcid:PMC7403106 fatcat:ufujm5bzyndqflhrcxd2aqjwzu