ArticleScientific reports2021
Prediction of individual COVID-19 diagnosis using baseline demographics and lab data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Comparative analysis of frequentist, Bayesian, and machine learning models for predicting SARS-CoV-2 PCR positivity.Frontiers in artificial intelligence · 2025Article
- Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features.NPJ digital medicine · 2024Article
- Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.Healthcare (Basel, Switzerland) · 2024Article
- Article
- RApid Throughput Screening for Asymptomatic COVID-19 Infection With an Electrocardiogram: A Prospective Observational Study.Mayo Clinic proceedings. Digital health · 2023Article
- Predictors of COVID-19 severity among pregnant patients.Bosnian journal of basic medical sciences · 2022Article
- An integrated framework for identifying clinical-laboratory indicators for novel pandemics: COVID-19 and MIS-C.NPJ digital medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.
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Registered trials
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