Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease Article Swipe
Joshua Smith
,
Brian D. Williamson
,
David Cronkite
,
Daniel Park
,
Jill M Whitaker
,
Michael F McLemore
,
Joshua T Osmanski
,
Robert Winter
,
Arvind Ramaprasan
,
Ann E. Kelley
,
Mary Shea
,
Saranrat Wittayanukorn
,
Danijela Stojanović
,
Yueqin Zhao
,
Sengwee Toh
,
Kevin B. Johnson
,
David M. Aronoff
,
David Carrell
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocad241
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocad241
Objectives Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions. Materials and methods PheNorm is a general-purpose automated approach to creating computable phenotype algorithms based on natural language processing, machine learning, and (low cost) silver-standard training labels. We applied PheNorm to cohorts of potential COVID-19 patients from 2 institutions and used gold-standard manual chart review data to investigate the impact on performance of alternative feature engineering options and implementing externally trained models without local retraining. Results Models at each institution achieved AUC, sensitivity, and positive predictive value of 0.853, 0.879, 0.851 and 0.804, 0.976, and 0.885, respectively, at quantiles of model-predicted risk that maximize F1. We report performance metrics for all combinations of silver labels, feature engineering options, and models trained internally versus externally. Discussion Phenotyping algorithms developed using PheNorm performed well at both institutions. Performance varied with different silver-standard labels and feature engineering options. Models developed locally at one site also worked well when implemented externally at the other site. Conclusion PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches.
Related Topics
Concepts
Feature engineering
Computer science
Machine learning
Artificial intelligence
Feature (linguistics)
Algorithm
Overhead (engineering)
Gold standard (test)
Coronavirus disease 2019 (COVID-19)
Data mining
Medicine
Disease
Deep learning
Mathematics
Statistics
Operating system
Philosophy
Pathology
Infectious disease (medical specialty)
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocad241
- OA Status
- green
- Cited By
- 7
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389903773
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4389903773Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/jamia/ocad241Digital Object Identifier
- Title
-
Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 diseaseWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-18Full publication date if available
- Authors
-
Joshua Smith, Brian D. Williamson, David Cronkite, Daniel Park, Jill M Whitaker, Michael F McLemore, Joshua T Osmanski, Robert Winter, Arvind Ramaprasan, Ann E. Kelley, Mary Shea, Saranrat Wittayanukorn, Danijela Stojanović, Yueqin Zhao, Sengwee Toh, Kevin B. Johnson, David M. Aronoff, David CarrellList of authors in order
- Landing page
-
https://doi.org/10.1093/jamia/ocad241Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10873852/pdf/ocad241.pdfDirect OA link when available
- Concepts
-
Feature engineering, Computer science, Machine learning, Artificial intelligence, Feature (linguistics), Algorithm, Overhead (engineering), Gold standard (test), Coronavirus disease 2019 (COVID-19), Data mining, Medicine, Disease, Deep learning, Mathematics, Statistics, Operating system, Philosophy, Pathology, Infectious disease (medical specialty), LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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