Data science for extubation prediction and value of information in surgical intensive care unit
Journal
Journal of Clinical Medicine
Journal Volume
8
Journal Issue
10
Date Issued
2019
Author(s)
Abstract
Besides the traditional indices such as biochemistry, arterial blood gas, rapid shallow breathing index (RSBI), acute physiology and chronic health evaluation (APACHE) II score, this study suggests a data science framework for extubation prediction in the surgical intensive care unit (SICU) and investigates the value of the information our prediction model provides. A data science framework including variable selection (e.g., multivariate adaptive regression splines, stepwise logistic regression and random forest), prediction models (e.g., support vector machine, boosting logistic regression and backpropagation neural network (BPN)) and decision analysis (e.g., Bayesian method) is proposed to identify the important variables and support the extubation decision. An empirical study of a leading hospital in Taiwan in 2015–2016 is conducted to validate the proposed framework. The results show that APACHE II and white blood cells (WBC) are the two most critical variables, and then the priority sequence is eye opening, heart rate, glucose, sodium and hematocrit. BPN with selected variables shows better prediction performance (sensitivity: 0.830; specificity: 0.890; accuracy 0.860) than that with APACHE II or RSBI. The value of information is further investigated and shows that the expected value of experimentation (EVE), 0.652 days (patient staying in the ICU), is saved when comparing with current clinical experience. Furthermore, the maximal value of information occurs in a failure rate around 7.1% and it reveals the “best applicable condition” of the proposed prediction model. The results validate the decision quality and useful information provided by our predicted model. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Data mining; Extubation; Machine learning; Precision medicine; Surgical intensive care unit
SDGs
Other Subjects
glucose; sodium; analysis of variance; APACHE; arterial gas; Article; back propagation neural network; Bayesian learning; computer model; data mining; diagnostic accuracy; evaluation study; extubation; false negative result; false positive result; heart rate; hematocrit; human; leukocyte count; mathematical model; multivariate logistic regression analysis; nonhuman; personalized medicine; prediction; random forest; rapid shallow breathing index; sensitivity analysis; sensitivity and specificity; support vector machine; surgical intensive care unit; validation study
Type
journal article