# Automated Knowledge Engineering Methods to Improve Consumers' Comprehension of their Health Records

> **NIH NIH F31** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $47,355

## Abstract

PROJECT SUMMARY
 Today, more patients can access their health records online than ever before. However, clinical acronyms
hinder patients' comprehension of their records and decrease the benefits of transparency. An automated
system for expanding clinical acronyms should have major clinical significance and far-reaching consequences
for improving patient-provider communication, shared decision-making, and health outcomes. Existing systems
have limited power to expand clinical acronyms, primarily due to the lack of comprehensiveness (or generali-
zability) of existing acronym sense inventories. Because developing comprehensive sense inventories is
difficult, existing knowledge engineering methods have primarily focused on developing institution-specific
sense inventories. Institution-specific sense inventories may not be generalizable to other geographical regions
and medical specialties. Furthermore, developing an institution-specific sense inventory at every US healthcare
organization is not feasible, especially without automated methods which currently do not exist.
 I developed advanced knowledge engineering methods to overcome these limitations through the use of
fully automated techniques to generalize existing sense inventories from different geographical regions and
medical specialties. My methods leverage the extensive resources already devoted to developing institution-
specific sense inventories in the U.S., and may help generalize existing sense inventories to institutions without
the resources to develop them. Although promising, challenges remain with the optimization and evaluation of
these methods. The objective of the proposed project is to use knowledge engineering to improve patients'
comprehension of their health records, focusing specifically on clinical acronyms. In Aim 1, I will develop new
knowledge engineering methods to facilitate the automated integration of sense inventories, using literature-
based quality heuristics and a Siamese neural network to establish synonymy. I will evaluate these methods
using multiple metrics to assess redundancy, quality, and coverage in two test corpora with over 17 million
clinical notes. In Aim 2, I will evaluate whether the knowledge engineering methods improve comprehension of
doctors' notes in 60 hospitalized patients with advanced heart failure. With success, I will create novel,
automated knowledge engineering methods that can be directly applied to improve patient care. This research
is in support of my mentored doctoral training at Columbia University Department of Biomedical Informatics
(DBMI) under Drs. David Vawdrey, George Hripcsak, Carol Friedman, Suzanne Bakken, and Chunhua Weng,
and will include coursework on deep learning, oral presentations at major annual conferences, and career
development planning, among other activities. DBMI is frequently recognized as one of the oldest and best
programs of its kind in the world, and provides an exception training environment for my ...

## Key facts

- **NIH application ID:** 9895430
- **Project number:** 5F31LM013054-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Lisa Grossman Liu
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $47,355
- **Award type:** 5
- **Project period:** 2019-03-01 → 2021-01-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9895430

## Citation

> US National Institutes of Health, RePORTER application 9895430, Automated Knowledge Engineering Methods to Improve Consumers' Comprehension of their Health Records (5F31LM013054-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9895430. Licensed CC0.

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