Family history is considered a risk factor for many diseases because it
implicitly captures shared genetic, environmental and lifestyle factors. A
nationwide electronic health record (EHR) system spanning multiple generations
presents new opportunities for studying a connected network of medical
histories for entire families. In this work we present a graph-based deep
learning approach for learning explainable, supervised representations of how
each family member's longitudinal medical history influences a patient's
disease risk. We demonstrate that this approach is beneficial for predicting
10-year disease onset for 5 complex disease phenotypes, compared to
clinically-inspired and deep learning baselines for a nationwide EHR system
comprising 7 million individuals with up to third-degree relatives. Through the
use of graph explainability techniques, we illustrate that a graph-based
approach enables more personalized modeling of family information and disease
risk by identifying important relatives and features for prediction