A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.
JAMIA Open
; 4(3): ooab070, 2021 Jul.
Article
in English
| MEDLINE | ID: covidwho-1369113
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 ANDMETHODS:
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
/
Qualitative research
Topics:
Long Covid
Language:
English
Journal:
JAMIA Open
Year:
2021
Document Type:
Article
Affiliation country:
Jamiaopen
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