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

开发了 FIT (针对患者找到重要术语) 系统，该系统使用无监督的自然语言处理方法，将医学术语在电子病历笔记中排序，以便患者更好理解，性能优于其他三种集成排名器，相对于其参数，它的性能比较不敏感，可应用于其他领域。