Prediction of Post-Intubation Tachycardia Using Machine-Learning Models

Applied Sciences

SangHyun Kim¹  Woohyun Jung¹  YangHoon Chung¹  BonSung Koo¹  Young-Seob Jeong²  AhReum Kang²  Hanna Kim² 

[1] Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea;SCH Media Labs, Soonchunhyang University, Asan 31538, Korea;

关键词: tachycardia prediction;    tracheal intubation;    electronic medical record;    vital sign;    machine learning;    clinical decision support;   

DOI  :  10.3390/app10031151

来源: DOAJ

【 摘 要 】

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 naïve 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.

【 授权许可】

Unknown