Mining Concepts for a COVID Interface Terminology for Annotation of EHRs

Vipina K. Keloth, Shuxin Zhou, Luke Lindemann, Gai Elhanan, Andrew J. Einstein, James Geller, Yehoshua Perl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The COVID-19 pandemic has overwhelmed the healthcare services of many countries with increased number of patients and also with a deluge of medical data. Furthermore, the emergence and global spread of new infectious diseases are highly likely to continue in the future. Incomplete data about presentations, signs, and symptoms of COVID-19 has had adverse effects on healthcare delivery. The EHRs of US hospitals have ingested huge volumes of relevant, up-to-date data about patients, but the lack of a proper system to annotate this data has greatly reduced its usefulness. We propose to design a COVID interface terminology for the annotation of EHR notes of COVID-19 patients. The initial version of this interface terminology was created by integrating COVID concepts from existing ontologies. Further enrichment of the interface terminology is performed by mining high granularity concepts from EHRs, because such concepts are usually not present in the existing reference terminologies. We use the techniques of concatenation and anchoring iteratively to extract high granularity phrases from the clinical text. In addition to increasing the conceptual base of the COVID interface terminology, this will also help in generating training data for large scale concept mining using machine learning techniques. Having the annotated clinical notes of COVID-19 patients available will help in speeding up research in this field.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3753-3760
Number of pages8
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
CountryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Keywords

  • concept mining
  • COVID-19 ontologies
  • COVID-19 patient EHRs
  • EHR annotation
  • interface terminology

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