Method of non-invasive parameters for predicting the probability of early in-hospital death of patients in intensive care unit. (March 2022)

• Record Type: Journal Article
• Authors: Wang, Pingan; Xu, Jiameng; Wang, Chengyi; Zhang, Guang; Wang, Huiquan
• Is Part Of: Biomedical signal processing and control. Volume 73(2022)
• Publication Date: 2022-03
• Subjects: Probability of death in ICU patients -- Non-invasive -- Extreme Gradient Boosting; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103405
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Objectives: To achieve timely and accurate prediction of the death probability of intensive care unit (ICU) patients without the invasive physiological parameters of professional medical staff and patients, a new method for predicting the death probability of ICU patients based on Extreme Gradient Boosting (XGBoost) machine learning is proposed. Methods: 15 non-invasive physiological parameters all from Medical Information Mart for Intensive Care III (MIMIC III) database were used in this study, and a total of 151 characteristic variables were extracted. And based on the XGBoost machine learning method, the ten-fold cross-validation method and the cost-sensitive classification threshold adjustment method are used to optimize the model parameters to establish the optimal prediction model of patient death probability. Result: In the case of using the entire feature set, the optimal prediction results of the XGBoost machine learning model are 0.858 for accuracy (ACC) and 0.941 for area under receiver operating characteristic curve (AUC). Better than traditional scoring methods (the ACC and AUC of the optimal prediction results of the traditional scoring method are 0.736 and 0.816, respectively). Conclusions: Compared with the traditional scoring system, the machine learning algorithm proposed in this study uses the patient's non-invasive physiological parameters, and can provide a more accurate prediction of the death probability of ICU patients in a timely manner without medical professionals and invasive physiological parameters. This makes it possible for the death probability of ICU patients' prediction to be widely used and timely and appropriate clinical decision-making possible.