A machine learning method for acute hypotensive episodes prediction using only non-invasive parameters. (March 2021)

• Record Type: Journal Article
• Authors: Zhang, Guang; Yuan, Jing; Yu, Ming; Wu, Taihu; Luo, Xi; Chen, Feng
• Is Part Of: Computer methods and programs in biomedicine. Volume 200(2021)
• Publication Date: 2021-03
• Subjects: Acute hypotensive episodes (AHE) -- Non-invasive physiological parameters (NIPPs) -- Machine learning algorithms -- Observation window -- Prediction gap -- Feature extraction methods -- Prediction -- Data mining; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105845
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Only easily obtained noninvasive parameters were used for acute hypotension episodes prediction, allowing the model to be applied in pre-hospital first aid or battlefield environments. Appropriate length of observation window were selected after comparative analysis, and the onset of acute hypotension episodes was predicted 1 hour in advance, which can leave enough time for clinicians to take intervention. The optimal features were selected for modeling and an ensemble model were proposed by integrating three traditional machine learning algorithms to improve the prediction performance, which can make the model easily be integrated in emergency equipment and provide more robust advice in pre-hospital first-aid. Abstract: Background and objectives: Accurate prediction of acute hypotensive episodes (AHE) is fundamentally important for timely and appropriate clinical decision-making, as it can provide medical professionals with sufficient time to accurately select more efficient therapeutic interventions for each specific condition. However, existing methods are invasive, easily affected by artifacts and can be difficult to acquire in a pre-hospital setting. Methods: In this study, 1055 patients' records were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database (MIMIC II), comprising of 388 AHE records and 667 non-AHE records. Six commonly used machine learning algorithms were selected and used to develop an AHE prediction model based on features extracted from seven types of non-invasive physiological parameters. Results: The optimal observation window and prediction gap were selected as 300 minutes and 60 minutes, respectively. For GBDT, XGB and AdaBoost, the optimal feature subsets contained only 39% of the overall features. An ensemble prediction model was developed using the voting method to achieve a more robust performance with an accuracy (ACC) of 0.822 and area under the receiver operating characteristic curve (AUC) of 0.878. Conclusion: A novel machine learning method that uses only noninvasive physiological parameters offers a promising solution for easy and prompt AHE prediction in widespread scenario applications, including pre-hospital and in-hospital care.