  Predicting the health risks of patients using Electronic Health Records (EHR)
has attracted considerable attention in recent years, especially with the
development of deep learning techniques. Health risk refers to the probability
of the occurrence of a specific health outcome for a specific patient. The
predicted risks can be used to support decision-making by healthcare
professionals. EHRs are structured patient journey data. Each patient journey
contains a chronological set of clinical events, and within each clinical
event, there is a set of clinical/medical activities. Due to variations of
patient conditions and treatment needs, EHR patient journey data has an
inherently high degree of missingness that contains important information
affecting relationships among variables, including time. Existing deep
learning-based models generate imputed values for missing values when learning
the relationships. However, imputed data in EHR patient journey data may
distort the clinical meaning of the original EHR patient journey data,
resulting in classification bias. This paper proposes a novel end-to-end
approach to modeling EHR patient journey data with Integrated Convolutional and
Recurrent Neural Networks. Our model can capture both long- and short-term
temporal patterns within each patient journey and effectively handle the high
degree of missingness in EHR data without any imputation data generation.
Extensive experimental results using the proposed model on two real-world
datasets demonstrate robust performance as well as superior prediction accuracy
compared to existing state-of-the-art imputation-based prediction methods.
