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

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

SUBMITTER: Sahoo HS 

PROVIDER: S-EPMC8374371 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature