Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data

Wang, Shaodong; Jiang, Yiqun; Li, Qing; Zhang, Wenli

Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data

File

2024-Li-TimelyICU.pdf (980.82 KB)

Date

2024-06-18

Authors

Wang, Shaodong

Jiang, Yiqun

Li, Qing

Zhang, Wenli

Publisher

IEEE Journal of Biomedical and Health Informatics

Department

Industrial and Manufacturing Systems Engineering

Information Systems and Business Analytics

Abstract

The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed by critical care needs. Existing research faces challenges such as feature extraction difficulties, low accuracy, and resource-intensive features. Some studies have explored deep learning models that utilize raw clinical inputs. However, these models are considered non-interpretable black boxes, which prevents their wide application. The objective of the study is to develop a new method using stochastic signal analysis and machine learning techniques to effectively extract features with strong predictive power from ICU patients' real-time time series of vital signs for accurate and timely ICU outcome prediction. The results show the proposed method extracted meaningful features and outperforms baseline methods, including APACHE IV (AUC = 0.750), deep learning-based models (AUC = 0.732, 0.712, 0.698, 0.722), and statistical feature classification methods (AUC = 0.765) by a large margin (AUC = 0.869). The proposed method has clinical, management, and administrative implications since it enables healthcare professionals to identify deviations from prognostications timely and accurately and, therefore, to conduct proper interventions.

Comments

This is a manuscript of the article Published as S. Wang, Y. Jiang, Q. Li and W. Zhang, "Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data," in IEEE Journal of Biomedical and Health Informatics. doi: https://doi.org/10.1109/JBHI.2024.3416039. © 2024 IEEE. Posted with Permission.