Predicting critical state after COVID-19 diagnosis: Model development using a large US electronic health record dataset
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Abstract
As the COVID-19 pandemic is challenging healthcare systems worldwide, early identification of patients with a high risk of complication is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (IBM Explorys), including demographics, comorbidities, symptoms, and hospitalization. Out of 15753 COVID-19 patients, 2050 went into critical state or deceased. Non-random train-test splits by time were repeated 100 times and led to a ROC AUC of 0.861 [0.838, 0.883] and a precision-recall AUC of 0.434 [0.414, 0.485] (median and interquartile range). The interpretability analysis confirmed evidence on major risk factors (e.g., older age, higher BMI, male gender, diabetes, and cardiovascular disease) in an efficient way compared to clinical studies, demonstrating the model validity. Such personalized predictions could enable fine-graded risk stratification for optimized care management.
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The prognostic model for predicting critical state after COVID-19 diagnosis achieved a ROC AUC of 0.861 [0.838, 0.883].
“ROC AUC: 0.861 [0.838, 0.883].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [79]
The prognostic model for predicting critical state after COVID-19 diagnosis achieved a precision-recall AUC of 0.434 [0.414, 0.485].
“Precision-recall AUC: 0.434 [0.414, 0.485]”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [79]
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Flagged first, then by citations
Research works printed but not linked: 1
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- Authors
- Rinderknecht, M. D., & Klopfenstein, Y.
- Corresponding author
- Klopfenstein, Yannick
- Venue
- npj Digital Medicine
- Published
- 2021-07-20
- Volume
- 4
- Issue
- 1
- Pages
- 113-113
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC8292360
- OpenAlex
- W3186718770
- Cited by
- 23
- References
- 76
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 7 · 2022: 5 · 2021: 1
- FWCI
- 1.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- COVID-19 pandemic, critical illness prediction, prognostic model, risk stratification, electronic health records, comorbidities, diabetes mellitus, cardiovascular disease, body mass index, age, mortality, model interpretability
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