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Predicting the health risks of patients using Electronic Health Records (EHR)has attracted considerable attention in recent years, especially with thedevelopment of deep learning techniques. Health risk refers to the probabilityof the occurrence of a specific health outcome for a specific patient. Thepredicted risks can be used to support decision-making by healthcareprofessionals. EHRs are structured patient journey data. Each patient journeycontains a chronological set of clinical events, and within each clinicalevent, there is a set of clinical/medical activities. Due to variations ofpatient conditions and treatment needs, EHR patient journey data has aninherently high degree of missingness that contains important informationaffecting relationships among variables, including time. Existing deeplearning-based models generate imputed values for missing values when learningthe relationships. However, imputed data in EHR patient journey data maydistort the clinical meaning of the original EHR patient journey data,resulting in classification bias. This paper proposes a novel end-to-endapproach to modeling EHR patient journey data with Integrated Convolutional andRecurrent Neural Networks. Our model can capture both long- and short-termtemporal patterns within each patient journey and effectively handle the highdegree of missingness in EHR data without any imputation data generation.Extensive experimental results using the proposed model on two real-worlddatasets demonstrate robust performance as well as superior prediction accuracycompared to existing state-of-the-art imputation-based prediction methods.

Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values