  Family history is considered a risk factor for many diseases because it
implicitly captures shared genetic, environmental and lifestyle factors.
Finland's 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 Finland's 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.
