Data Science and Machine Learning in Anesthesiology.

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. PMID: 32209960 [PubMed - as supplied by publisher]
Source: Korean Journal of Anesthesiology - Category: Anesthesiology Tags: Korean J Anesthesiol Source Type: research