An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)

• URL: http://arxiv.org/abs/2110.10780v1
• Date: Wed, 20 Oct 2021 21:09:41 GMT
• Title: An Open Natural Language Processing Development 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, Rui Zhang, John D. Osborne, Masoud Rouhizadeh, Yongqun He, Emily Pfaff, Christopher G. Chute, Tim Duong, Melissa A. Haendel, Rafael Fuentes, Peter Szolovits, Hua Xu, Hongfang Liu (N3C Natural Language Processing (NLP) Subgroup)
• Abstract summary: We propose an open natural language processing development framework and evaluate it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C) Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects.
• Score: 29.701601520785033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to adopt NLP models due to limited transparency, Interpretability and usability. Built upon our previous work, in this study, we proposed an open natural language processing development framework and evaluated it through the implementation of NLP algorithms for the National COVID Cohort Collaborative (N3C). Based on the interests in information extraction from COVID-19 related clinical notes, our work includes 1) an open data annotation process using COVID-19 signs and symptoms as the use case, 2) a community-driven ruleset composing platform, and 3) a synthetic text data generation workflow to generate texts for information extraction tasks without involving human subjects. The generated corpora derived out of the texts from multiple intuitions and gold standard annotation are tested on a single institution's rule set has the performances in F1 score of 0.876, 0.706 and 0.694, respectively. The study as a consortium effort of the N3C NLP subgroup demonstrates the feasibility of creating a federated NLP algorithm development and benchmarking platform to enhance multi-institution clinical NLP study.

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