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Data science and machine learning in anesthesiology

Authors
 Dongwoo Chae 
Citation
 KOREAN JOURNAL OF ANESTHESIOLOGY, Vol.73(4) : 285-295, 2020-08 
Journal Title
 KOREAN JOURNAL OF ANESTHESIOLOGY 
ISSN
 2005-6419 
Issue Date
2020-08
Keywords
Artificial intelligence ; Data science ; Electronic health record ; Machine learning ; Predictive analytics ; Risk score system
Abstract
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 researcher typically tests various ML models and compares the predictive performance with the reference logistic regression model. The main advantage of ML lies in its ability to deal with many features with complex interactions and its specific focus on maximizing predictive performance. However, emphasis on data-driven prediction can sometimes neglect mechanistic understanding. This article mainly focuses on the application of supervised ML to electronic health record (EHR) data. The main limitation of EHR-based studies is in the difficulty of establishing causal relationships. However, the associated 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 also provided. Towards the end, several examples of successful ML applications in anesthesiology are discussed. The goal of this article is to provide a basic roadmap to novice ML researchers working in the field of anesthesiology.
Files in This Item:
T202004259.pdf Download
DOI
10.4097/kja.20124
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pharmacology (약리학교실) > 1. Journal Papers
Yonsei Authors
Chae, Dong Woo(채동우) ORCID logo https://orcid.org/0000-0002-7675-3821
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/180168
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