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Increasingly large electronic health records (EHRs) provide an opportunity toalgorithmically learn medical knowledge. In one prominent example, a causalhealth knowledge graph could learn relationships between diseases and symptomsand then serve as a diagnostic tool to be refined with additional clinicalinput. Prior research has demonstrated the ability to construct such a graphfrom over 270,000 emergency department patient visits. In this work, wedescribe methods to evaluate a health knowledge graph for robustness. Movingbeyond precision and recall, we analyze for which diseases and for whichpatients the graph is most accurate. We identify sample size and unmeasuredconfounders as major sources of error in the health knowledge graph. Weintroduce a method to leverage non-linear functions in building the causalgraph to better understand existing model assumptions. Finally, to assess modelgeneralizability, we extend to a larger set of complete patient visits within ahospital system. We conclude with a discussion on how to robustly extractmedical knowledge from EHRs.

Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph