Integrating Static and Time-Series Data in Deep Recurrent Models for Oncology Early Warning Systems

Abstract

Machine learning techniques have shown promise in predicting clinical deterioration of hospitalized patients based on electronic health record (EHR). However, building accurate early warning systems (EWS) remains challenging in practice. EHRs are heterogeneous, comprising both static and time-series data. Moreover, missing values are prevalent in both static and time-series data, and the missingness of certain data can be correlated to clinical outcomes. This paper proposes a novel approach for integrating static and time-series clinical data in deep recurrent models through multimodal fusion. Furthermore, we exploit the correlation of static and time-series data through cross-modal imputation in an integrated recurrent model. We apply the proposed approaches to a dataset extracted from the EHR of 20,700 hospitalizations of adult oncology patients in a research hospital. The experiments demonstrate the proposed approaches outperform the state-of-the-art models in terms of predictive accuracy in generating early warnings for clinical deterioration. A case study further establishes the efficacy of the predictive model for early warning systems under realistic clinical settings.