A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification

Himanshu S Sahoo, Greg M Silverman, Nicholas E Ingraham, Monica I Lupei, Michael A Puskarich, Raymond L Finzel, John Sartori, Rui Zhang, Benjamin C Knoll, Sijia Liu, Hongfang Liu, Genevieve B Melton, Christopher J Tignanelli, Serguei V S Pakhomov

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Objective: With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.

Materials and Methods: Performance, resource utilization, and runtime of the rule-based gazetteer were compared with five annotation systems: BioMedICUS, cTAKES, MetaMap, CLAMP, and MedTagger.

Results: This rule-based gazetteer was the fastest, had a low resource footprint, and similar performance for weighted microaverage and macroaverage measures of precision, recall, and f1-score compared to other annotation systems.

Discussion: Opportunities to increase its performance include fine-tuning lexical rules for symptom identification. Additionally, it could run on multiple compute nodes for faster runtime.

Conclusion: This rule-based gazetteer overcame key technical limitations facilitating real-time symptomatology identification for COVID-19 and integration of unstructured data elements into our CDS. It is ideal for large-scale deployment across a wide variety of healthcare settings for surveillance of acute COVID-19 symptoms for integration into prognostic modeling. Such a system is currently being leveraged for monitoring of postacute sequelae of COVID-19 (PASC) progression in COVID-19 survivors. This study conducted the first in-depth analysis and developed a rule-based gazetteer for COVID-19 symptom extraction with the following key features: low processor and memory utilization, faster runtime, and similar weighted microaverage and macroaverage measures for precision, recall, and f1-score compared to industry-standard annotation systems.

Original languageEnglish (US)
Article numberooab070
Pages (from-to)ooab070
JournalJAMIA Open
Volume4
Issue number3
DOIs
StatePublished - Jul 1 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Keywords

  • and symptoms
  • artificial intelligence
  • clinical decision support systems
  • follow-up studies
  • information extraction
  • Natural language processing
  • signs

PubMed: MeSH publication types

  • Journal Article

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