Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 8th Machine Learning for Healthcare Conference
EditorsKaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary Lipton, Rajesh Ranganath, Iñigo Urteaga, Serene Yeung
PublisherJMLR
Pages824-845
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventMachine Learning for Healthcare - New York, United States
Duration: 11 Aug 202312 Aug 2023
https://www.mlforhc.org/2023-agenda

Publication series

NameProceedings of Machine Learning Research
Volume219
ISSN (Print)2640-3498

Conference

ConferenceMachine Learning for Healthcare
Abbreviated titleMLHC
Country/TerritoryUnited States
CityNew York
Period11/08/202312/08/2023
Internet address

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