Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction. (June 2023)

• Record Type: Journal Article
• Authors: Yin, Yilin; Chou, Chun-An
• Is Part Of: Computer methods and programs in biomedicine. Volume 235(2023)
• Publication Date: 2023-06
• Subjects: Critical care -- Survival analysis -- Dynamic learning -- Auto-regressive model -- Markov model -- Electronic health records; 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.2023.107545
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We investigate a multi-event survival analysis problem using multimodal EHR data for ICU patient cohorts with complications of acute respiratory distress syndrome and cardiovascular disease cases, respectively. We develop a new autoregressive dynamic learning model for estimating the mortality risks in association with complications. . Abstract: Background and objective: Survival analysis is widely applied for assessing the expected duration of patient status towards event occurrences such as mortality in healthcare domain, which is generally considered as a time-to-event problem. Patients with multiple complications have high mortality risks and oftentimes require specific intensive care and clinical treatments. The progression of complications is time-varying according to disease development and intrinsic interactions between complications with respect to mortality are uncertain. Classical methods for mortality prediction and survival analysis in critical care, such as risk scoring systems and cause-specific survival models, were not designed for this multi-event survival analysis problem and able to measure the competing risks of death for mutually exclusive events. In addition, multivariate temporal information of complications is not taken into consideration while estimating differentiated mortality risks in the early stage. Methods: In this paper, we propose a novel multi-event survival analysis solution using a tree-based autoregressive survival model of multi-modal electronic health record data. Specifically, we focus on modeling the temporal trajectory of complications and estimating the mortality risk associated with multiple potential complications simultaneously. In dynamic modeling, no assumptions are made for the relationships between time-dependent variables and risk transition over time. Results: Validated with the eICU database, our model achieves a better prediction performance with C-index ranging in 74–80%, compared to state-of-the-art machine learning methods in the literature, for the complications of acute respiratory distress syndrome and cardiovascular disease cases. Conclusions: Our model provides the distinguishable mortality risk curves over time for specific complications and the track of risk development that could potentially support the ICU resource reallocation.