Journal
JAMIA OPEN
Volume 4, Issue 3, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/jamiaopen/ooab070
Keywords
natural language processing; clinical decision support systems; artificial intelligence; information extraction; signs; and symptoms; follow-up studies
Funding
- Fairview Health Services
- National Institutes of Health's National Center for Advancing Translational Sciences [U01TR002062]
- National Institutes of Health's National Heart, Lung, and Blood Institute [T32HL07741]
- Agency for Healthcare Research and Quality (AHRQ) [R01HS026743]
- Patient-Centered Outcomes Research Institute (PCORI) [K12HS026379]
- University of Minnesota Office of Academic Clinical Affairs
- Division of Health Policy and Management, University of Minnesota School of Public Health
- University of Minnesota CTSA [UL1TR000114]
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The rule-based gazetteer developed in this study showed superior speed, resource utilization, and performance, providing an effective solution for real-time symptom identification and integration of unstructured data elements into clinical decision support systems. Fine-tuning lexical rules and running on multiple compute nodes were identified as opportunities to further enhance its performance.
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.
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