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Background: Electronic health record (EHR) notes contain abundant medicaljargon that can be difficult for patients to comprehend. One way to helppatients is to reduce information overload and help them focus on medical termsthat matter most to them.  Objective: The aim of this work was to develop FIT (Finding Important Termsfor patients), an unsupervised natural language processing (NLP) system thatranks medical terms in EHR notes based on their importance to patients.  Methods: We built FIT on a new unsupervised ensemble ranking model derivedfrom the biased random walk algorithm to combine heterogeneous informationresources for ranking candidate terms from each EHR note. Specifically, FITintegrates four single views for term importance: patient use of medicalconcepts, document-level term salience, word-occurrence based term relatedness,and topic coherence. It also incorporates partial information of termimportance as conveyed by terms' unfamiliarity levels and semantic types. Weevaluated FIT on 90 expert-annotated EHR notes and compared it with threebenchmark unsupervised ensemble ranking methods.  Results: FIT achieved 0.885 AUC-ROC for ranking candidate terms from EHRnotes to identify important terms. When including term identification, theperformance of FIT for identifying important terms from EHR notes was 0.813AUC-ROC. It outperformed the three ensemble rankers for most metrics. Itsperformance is relatively insensitive to its parameter.  Conclusions: FIT can automatically identify EHR terms important to patientsand may help develop personalized interventions to improve quality of care. Byusing unsupervised learning as well as a robust and flexible framework forinformation fusion, FIT can be readily applied to other domains andapplications.

Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients