An Open Natural Language Processing (NLP) Framework for Ehr-Based Clinical Research: A Case Demonstration Using the National COVID Cohort Collaborative (N3c)

Authors

Sijia Liu
Andrew Wen
Liwei Wang
Huan He
Sunyang Fu
Robert Miller
Andrew Williams
Daniel Harris
Ramakanth Kavuluru
Mei Liu
Noor Abu-El-Rub
Dalton Schutte
Rui Zhang
Masoud Rouhizadeh
John D Osborne
Yongqun He
Umit Topaloglu
Stephanie S Hong
Joel H Saltz
Thomas Schaffter
Emily Pfaff
Christopher G Chute
Tim Duong
Melissa A Haendel
Rafael Fuentes
Peter Szolovits
Hua Xu
Hongfang Liu

Publication Date

11-17-2023

Journal

Journal of the American Medical Informatics Association

Abstract

Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.

Keywords

Humans, Natural Language Processing, Electronic Health Records, COVID-19, Algorithms

Comments

Supplementary Materials

PMID: 37555837