  Objective: Allowing patients to access their own electronic health record
(EHR) notes through online patient portals has the potential to improve
patient-centered care. However, medical jargon, which abounds in EHR notes, has
been shown to be a barrier for patient EHR comprehension. Existing knowledge
bases that link medical jargon to lay terms or definitions play an important
role in alleviating this problem but have low coverage of medical jargon in
EHRs. We developed a data-driven approach that mines EHRs to identify and rank
medical jargon based on its importance to patients, to support the building of
EHR-centric lay language resources.
  Methods: We developed an innovative adapted distant supervision (ADS) model
based on support vector machines to rank medical jargon from EHRs. For distant
supervision, we utilized the open-access, collaborative consumer health
vocabulary, a large, publicly available resource that links lay terms to
medical jargon. We explored both knowledge-based features from the Unified
Medical Language System and distributed word representations learned from
unlabeled large corpora. We evaluated the ADS model using physician-identified
important medical terms.
  Results: Our ADS model significantly surpassed two state-of-the-art automatic
term recognition methods, TF*IDF and C-Value, yielding 0.810 ROC-AUC versus
0.710 and 0.667, respectively. Our model identified 10K important medical
jargon terms after ranking over 100K candidate terms mined from over 7,500 EHR
narratives.
  Conclusion: Our work is an important step towards enriching lexical resources
that link medical jargon to lay terms/definitions to support patient EHR
comprehension. The identified medical jargon terms and their rankings are
available upon request.
