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Family history is considered a risk factor for many diseases because itimplicitly captures shared genetic, environmental and lifestyle factors.Finland's nationwide electronic health record (EHR) system spanning multiplegenerations presents new opportunities for studying a connected network ofmedical histories for entire families. In this work we present a graph-baseddeep learning approach for learning explainable, supervised representations ofhow each family member's longitudinal medical history influences a patient'sdisease risk. We demonstrate that this approach is beneficial for predicting10-year disease onset for 5 complex disease phenotypes, compared toclinically-inspired and deep learning baselines for Finland's nationwide EHRsystem comprising 7 million individuals with up to third-degree relatives.Through the use of graph explainability techniques, we illustrate that agraph-based approach enables more personalized modeling of family informationand disease risk by identifying important relatives and features forprediction.

Characterizing personalized effects of family information on disease risk using graph representation learning