Academic Journal
The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe.
| Title: | The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe. |
|---|---|
| Authors: | Safdari, Ali, Keshav, Chanda Sai, Mody, Deepanshu, Verma, Kshitij, Kaushal, Utsav, Burra, Vaadeendra Kumar, Ray, Sibnath, Bandyopadhyay, Debashree |
| Source: | PLoS ONE; 2/4/2025, Vol. 20 Issue 2, p1-26, 26p |
| Subject Terms: | Machine learning, Hospital records, COVID-19 pandemic, Sample size (Statistics), Hospital utilization, Ethnicity |
| Abstract: | The unprecedented worldwide pandemic caused by COVID-19 has motivated several research groups to develop machine-learning based approaches that aim to automate the diagnosis or screening of COVID-19, in large-scale. The gold standard for COVID-19 detection, quantitative-Real-Time-Polymerase-Chain-Reaction (qRT-PCR), is expensive and time-consuming. Alternatively, haematology-based detections were fast and near-accurate, although those were less explored. The external-validity of the haematology-based COVID-19-predictions on diverse populations are yet to be fully investigated. Here we report external-validity of machine learning-based prediction scores from haematological parameters recorded in different hospitals of Brazil, Italy, and Western Europe (raw sample size, 195554). The XGBoost classifier performed consistently better (out of seven ML classifiers) on all the datasets. The working models include a set of either four or fourteen haematological parameters. The internal performances of the XGBoost models (AUC scores range from 84% to 97%) were superior to ML models reported in the literature for some of these datasets (AUC scores range from 84% to 87%). The meta-validation on the external performances revealed the reliability of the performance (AUC score 86%) along with good accuracy of the probabilistic prediction (Brier score 14%), particularly when the model was trained and tested on fourteen haematological parameters from the same country (Brazil). The external performance was reduced when the model was trained on datasets from Italy and tested on Brazil (AUC score 69%) and Western Europe (AUC score 65%); presumably affected by factors, like, ethnicity, phenotype, immunity, reference ranges, across the populations. The state-of-the-art in the present study is the development of a COVID-19 prediction tool that is reliable and parsimonious, using a fewer number of hematological features, in comparison to the earlier study with meta-validation, based on sufficient sample size (n = 195554). Thus, current models can be applied at other demographic locations, preferably, with prior training of the model on the same population. Availability: https://covipred.bits-hyderabad.ac.in/home; https://github.com/debashreebanerjee/CoviPred. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Safdari%2C+Ali%22">Safdari, Ali</searchLink><br /><searchLink fieldCode="AR" term="%22Keshav%2C+Chanda+Sai%22">Keshav, Chanda Sai</searchLink><br /><searchLink fieldCode="AR" term="%22Mody%2C+Deepanshu%22">Mody, Deepanshu</searchLink><br /><searchLink fieldCode="AR" term="%22Verma%2C+Kshitij%22">Verma, Kshitij</searchLink><br /><searchLink fieldCode="AR" term="%22Kaushal%2C+Utsav%22">Kaushal, Utsav</searchLink><br /><searchLink fieldCode="AR" term="%22Burra%2C+Vaadeendra+Kumar%22">Burra, Vaadeendra Kumar</searchLink><br /><searchLink fieldCode="AR" term="%22Ray%2C+Sibnath%22">Ray, Sibnath</searchLink><br /><searchLink fieldCode="AR" term="%22Bandyopadhyay%2C+Debashree%22">Bandyopadhyay, Debashree</searchLink> – Name: TitleSource Label: Source Group: Src Data: PLoS ONE; 2/4/2025, Vol. 20 Issue 2, p1-26, 26p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Hospital+records%22">Hospital records</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br /><searchLink fieldCode="DE" term="%22Sample+size+%28Statistics%29%22">Sample size (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Hospital+utilization%22">Hospital utilization</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The unprecedented worldwide pandemic caused by COVID-19 has motivated several research groups to develop machine-learning based approaches that aim to automate the diagnosis or screening of COVID-19, in large-scale. The gold standard for COVID-19 detection, quantitative-Real-Time-Polymerase-Chain-Reaction (qRT-PCR), is expensive and time-consuming. Alternatively, haematology-based detections were fast and near-accurate, although those were less explored. The external-validity of the haematology-based COVID-19-predictions on diverse populations are yet to be fully investigated. Here we report external-validity of machine learning-based prediction scores from haematological parameters recorded in different hospitals of Brazil, Italy, and Western Europe (raw sample size, 195554). The XGBoost classifier performed consistently better (out of seven ML classifiers) on all the datasets. The working models include a set of either four or fourteen haematological parameters. The internal performances of the XGBoost models (AUC scores range from 84% to 97%) were superior to ML models reported in the literature for some of these datasets (AUC scores range from 84% to 87%). The meta-validation on the external performances revealed the reliability of the performance (AUC score 86%) along with good accuracy of the probabilistic prediction (Brier score 14%), particularly when the model was trained and tested on fourteen haematological parameters from the same country (Brazil). The external performance was reduced when the model was trained on datasets from Italy and tested on Brazil (AUC score 69%) and Western Europe (AUC score 65%); presumably affected by factors, like, ethnicity, phenotype, immunity, reference ranges, across the populations. The state-of-the-art in the present study is the development of a COVID-19 prediction tool that is reliable and parsimonious, using a fewer number of hematological features, in comparison to the earlier study with meta-validation, based on sufficient sample size (n = 195554). Thus, current models can be applied at other demographic locations, preferably, with prior training of the model on the same population. Availability: https://covipred.bits-hyderabad.ac.in/home; https://github.com/debashreebanerjee/CoviPred. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1371/journal.pone.0316467 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Hospital records Type: general – SubjectFull: COVID-19 pandemic Type: general – SubjectFull: Sample size (Statistics) Type: general – SubjectFull: Hospital utilization Type: general – SubjectFull: Ethnicity Type: general Titles: – TitleFull: The external validity of machine learning-based prediction scores from hematological parameters of COVID-19: A study using hospital records from Brazil, Italy, and Western Europe. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Safdari, Ali – PersonEntity: Name: NameFull: Keshav, Chanda Sai – PersonEntity: Name: NameFull: Mody, Deepanshu – PersonEntity: Name: NameFull: Verma, Kshitij – PersonEntity: Name: NameFull: Kaushal, Utsav – PersonEntity: Name: NameFull: Burra, Vaadeendra Kumar – PersonEntity: Name: NameFull: Ray, Sibnath – PersonEntity: Name: NameFull: Bandyopadhyay, Debashree IsPartOfRelationships: – BibEntity: Dates: – D: 04 M: 02 Text: 2/4/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19326203 Numbering: – Type: volume Value: 20 – Type: issue Value: 2 Titles: – TitleFull: PLoS ONE Type: main |
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