Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.

ABSTRACT:

Background

Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, 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. Targeted education can then be developed to improve patient EHR comprehension and the quality of care.

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

SUBMITTER: Chen J 

PROVIDER: S-EPMC5505865 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature