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Cited by 5Machine Learning in HealthcareCOVID-19 Clinical Research StudiesArtificial Intelligence in Healthcare

Early outcome detection for COVID-19 patients

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Sîrbu, Alina, Barbieri, Greta, Faita, Francesco, Ferragina, Paolo, Gargani, Luna, Ghiadoni, Lorenzo, Priami, Corrado·2021·Scientific Reports
DOI 10.1038/s41598-021-97990-1PMC PMC8446000
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Abstract

With the outbreak of COVID-19 exerting a strong pressure on hospitals and health facilities, clinical decision support systems based on predictive models can help to effectively improve the management of the pandemic. We present a method for predicting mortality for COVID-19 patients. Starting from a large number of clinical variables, we select six of them with largest predictive power, using a feature selection method based on genetic algorithms and starting from a set of COVID-19 patients from the first wave. The algorithm is designed to reduce the impact of missing values in the set of variables measured, and consider only variables that show good accuracy on validation data. The final predictive model provides accuracy larger than 85% on test data, including a new patient cohort from the second COVID-19 wave, and on patients with imputed missing values. The selected clinical variables are confirmed to be relevant by recent literature on COVID-19.

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  • Alternative approaches to COVID-19 triage use radiological imaging, which is less readily available and involves patient exposure to ionising radiation, or infrequently requested laboratory predicting markers, such as IL-6.

    “By contrast, alternative approaches to COVID-19 triage use radiological imaging, which is less readily available and involves patient exposure to ionising radiation,22 or infrequently requested laboratory predicting markers, such as IL-6.23, 24”

    From Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening (2022) · cites this paper as [24]

    The Discussion says clinical variables were measured inconsistently across patients, and Table 7 (Results > Interpretability; page not available) lists IL6 among the variables considered. The paper does not specifically say IL-6 was infrequently requested or establish it as a predictive marker; its selected predictors do not include IL-6. The imaging clause is attached to a different citation. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Sîrbu, A., Barbieri, G., Faita, F., Ferragina, P., Gargani, L., Ghiadoni, L., & Priami, C.
Affiliations
  • University of Pisa (IT)
  • Istituto di Fisiologia Clinica (IT)
Corresponding author
Sîrbu, Alina
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2021-09-16
Volume
11
Issue
1
Pages
18464-18464
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-021-97990-1
PMC
PMC8446000
OpenAlex
W3200013141
Cited by
5
References
2
Citations by year
2023: 2 · 2022: 3
FWCI
0.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion, cc-by)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
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, mortality prediction, clinical decision support, genetic algorithm feature selection, missing data imputation, early outcome prediction, clinical factors, clinical prediction model
MeSH terms
Machine Learning, COVID-19, Algorithms, Humans, Models, Theoretical, Mortality, Cohort Studies, Decision Support Systems, Clinical

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