Prediction of Post-Intubation Tachycardia Using Machine-Learning Modelsopen access
- Authors
- Kim, Hanna; Jeong, Young-Seob; Kang, Ah Reum; Jung, Woohyun; Chung, Yang Hoon; Koo, Bon Sung; Kim, Sang Hyun
- Issue Date
- Feb-2020
- Publisher
- MDPI
- Keywords
- tachycardia prediction; tracheal intubation; electronic medical record; vital sign; machine learning; clinical decision support
- Citation
- Applied Sciences-basel, v.10, no.3
- Journal Title
- Applied Sciences-basel
- Volume
- 10
- Number
- 3
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/3125
- DOI
- 10.3390/app10031151
- ISSN
- 2076-3417
- Abstract
- Tachycardia is defined as a heart rate greater than 100 bpm for more than 1 min. Tachycardia often occurs after endotracheal intubation and can cause serious complication in patients with cardiovascular disease. The ability to predict post-intubation tachycardia would help clinicians by notifying a potential event to pre-treat. In this paper, we predict the potential post-intubation tachycardia. Given electronic medical record and vital signs collected before tracheal intubation, we predict whether post-intubation tachycardia will occur within 10 min. Of 1931 available patient datasets, 257 remained after filtering those with inappropriate data such as outliers and inappropriate annotations. Three feature sets were designed using feature selection algorithms, and two additional feature sets were defined by statistical inspection or manual examination. The five feature sets were compared with various machine learning models such as naive Bayes classifiers, logistic regression, random forest, support vector machines, extreme gradient boosting, and artificial neural networks. Parameters of the models were optimized for each feature set. By 10-fold cross validation, we found that an logistic regression model with eight-dimensional hand-crafted features achieved an accuracy of 80.5%, recall of 85.1%, precision of 79.9%, an F1 score of 79.9%, and an area under the receiver operating characteristic curve of 0.85.
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- Appears in
Collections - College of Medicine > Department of Anesthesiology > 1. Journal Articles
- SCH Media Labs > SCH미디어랩스_SCH융합과학연구소 > 1. Journal Articles
- SCH Media Labs > Department of Big Data Engineering > 1. Journal Articles
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