Objective Recent advances in deep learning show significant potential in analyzing continuous monitoring electronic health records (EHR) data for clinical outcome prediction. We aim to develop a Transformer-based, Encounter-level Clinical Outcome (TECO) model to predict mortality in the intensive care unit (ICU) using inpatient EHR data. Materials and Methods TECO was developed using multiple baseline and time-dependent clinical variables from 2579 hospitalized COVID-19 patients to predict ICU mortality, and was validated externally in an ARDS cohort (n=2799) and a sepsis cohort (n=6622) from the Medical Information Mart for Intensive Care (MIMIC)-IV. Model performance was evaluated based on area under the receiver operating characteristic (AUC) and compared with Epic Deterioration Index (EDI), random forest (RF), and extreme gradient boosting (XGBoost). Results In the COVID-19 development dataset, TECO achieved higher AUC (0.89-0.97) across various time intervals compared to EDI (0.86-0.95), RF (0.87-0.96), and XGBoost (0.88-0.96). In the two MIMIC testing datasets (EDI not available), TECO yielded higher AUC (0.65-0.76) than RF (0.57-0.73) and XGBoost (0.57-0.73). In addition, TECO was able to identify clinically interpretable features that were correlated with the outcome. Discussion TECO outperformed proprietary metrics and conventional machine learning models in predicting ICU mortality among COVID-19 and non-COVID-19 patients. Conclusions TECO demonstrates a strong capability for predicting ICU mortality using continuous monitoring data. While further validation is needed, TECO has the potential to serve as a powerful early warning tool across various diseases in inpatient settings.

The authors have declared no competing interest.

This study was supported in part by the National Institutes of Health under award number R01GM140012 (GX), R01DE030656 (GX), R01GM115473 (GX), U01CA249245 (GX), U01AI169298 (YX), R35GM136375 (YX), the Cancer Prevention and Research Institute of Texas (CPRIT RP180805: YX; CPRIT RP230330: GX), and the Texas Health Resources Clinical Scholars Program.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The institutional review boards at Texas Health Resources and the University of Texas Southwestern Medical Center approved this study (Protocol #STU-2020-0786; activated on 8/24/2020). All patient identifiers were removed before EHR data extraction.

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The COVID-19 dataset could not be shared publicly due to data and privacy protection policies at Texas Health Resources and UT Southwestern Medical Center. The MIMIC dataset is publicly available at https://physionet.org/content/mimiciv/2.2/.