A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification. Issue 3 (7th August 2021)

• Record Type: Journal Article
• Authors: Sahoo, Himanshu S; Silverman, Greg M; Ingraham, Nicholas E; Lupei, Monica I; Puskarich, Michael A; Finzel, Raymond L; Sartori, John; Zhang, Rui; Knoll, Benjamin C; Liu, Sijia; Liu, Hongfang; Melton, Genevieve B; Tignanelli, Christopher J; Pakhomov, Serguei V S
• Is Part Of: JAMIA open. Volume 4:Issue 3(2021)
• Publication Date: 2021-08-07
• Subjects: natural language processing -- clinical decision support systems -- artificial intelligence -- information extraction -- signs -- and symptoms -- follow-up studies; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooab070
• Languages: English
• ISSNs: 2574-2531

Abstract

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.