ArticleHealth information science and systems2021
External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count.
Article in Health information science and systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 28 citations in OpenAlex.
- Tribulations, Triumphs, and Governance: Shaping the Future of Artificial Intelligence in Healthcare.EJIFCC · 2025Article
- Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review.Journal of medical Internet research · 2025Article
- Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.Molecular diversity · 2025Review
- Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper.BMC medical informatics and decision making · 2025Article
- Article
- Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN.Health information science and systems · 2024Article
- Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings.Scientific reports · 2024Article
- Leukocyte cell population data as potential markers of COVID-19 disease characterization.Journal of medical biochemistry · 2023Article
- Article
- Use of Machine Learning and Routine Laboratory Tests for Diabetes Mellitus Screening.BioMed research international · 2022Article
- Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review.JMIR bioinformatics and biotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe rRT-PCR for COVID-19 diagnosis is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Routine hematochemical tests are a faster and less expensive alternative for diagnosis. Thus, Machine Learning (ML) has been applied to hematological parameters to develop diagnostic tools and help clinicians in promptly managing positive patients. However, few ML models have been externally validated, making their real-world applicability unclear.
methodsWe externally validate 6 state-of-the-art diagnostic ML models, based on Complete Blood Count (CBC) and trained on a dataset encompassing 816 COVID-19 positive cases. The external validation was performed based on two datasets, collected at two different hospitals in northern Italy and encompassing 163 and 104 COVID-19 positive cases, in terms of both error rate and calibration. RESULTS AND
conclusionWe report an average AUC of 95% and average Brier score of 0.11, out-performing existing ML methods, and showing good cross-site transportability. The best performing model (SVM) reported an average AUC of 97.5% (Sensitivity: 87.5%, Specificity: 94%), comparable with the performance of RT-PCR, and was also the best calibrated. The validated models can be useful in the early identification of potential COVID-19 patients, due to the rapid availability of CBC exams, and in multiple test settings.
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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.