Evidence map›Paper›PMID 34423261›Full record

ArticleJAMIA open2021

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 and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 1 country.

Himanshu S SahooDepartment of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0002-0862-6193
Greg M SilvermanDepartment of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
Nicholas E IngrahamPulmonary Disease and Critical Care Medicine, University of Minnesota, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0002-0292-0594
Monica I LupeiDepartment of Anesthesiology, University of Minnesota, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0002-8513-4061
Michael A PuskarichDepartment of Emergency Medicine, University of Minnesota, Minneapolis, Minnesota, USA.
Raymond L FinzelDepartment of Pharmaceutical Care and Health Systems, University of Minnesota, Minneapolis, Minnesota, USA.
John SartoriDepartment of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
Rui ZhangDepartment of Pharmaceutical Care and Health Systems, University of Minnesota, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0001-8258-3585
Benjamin C KnollInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Sijia LiuDepartment of Health Science Research, Mayo Clinic, Rochester, New York, USA.ORCID https://orcid.org/0000-0001-9763-1164
Hongfang LiuDepartment of Health Science Research, Mayo Clinic, Rochester, New York, USA.
Genevieve B MeltonDepartment of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
Christopher J TignanelliDepartment of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
Serguei V S PakhomovDepartment of Pharmaceutical Care and Health Systems, University of Minnesota, Minneapolis, Minnesota, USA.
University of Minnesota · USMayo Clinic in Florida · US

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR000114 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R · 2012 to 2015
$35.0M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
TRAINING-PULMONARY CELL &MOLECULAR BIOLOGY &PHYSIOLOGYT32HL007741 · NHLBI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI DUDLEY, R. ADAMS, INGBAR, DAVID H · 1994 to 2023
$13.9M
Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
AHRQ HHS K12 HS026379NCATS NIH HHS U01 TR002062NCATS NIH HHS UL1 TR000114NCATS NIH HHS UL1 TR002494NHLBI NIH HHS T32 HL007741
6 · The paper itself

Abstract

objectiveWith 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

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

resultsThis 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.

conclusionThis 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.

Indexed as

and symptomsartificial intelligenceclinical decision support systemsfollow-up studiesinformation extractionnatural language processingsigns

Identifiers

PMID34423261
PMCPMC8374371
OpenAlexW3195832406

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.