Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.
Clin Chem Lab Med
; 59(2): 421-431, 2020 10 21.
Article
in English
| MEDLINE | ID: covidwho-881170
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
Objectives:
The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative.Methods:
Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models the complete OSR dataset (72 features complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation.Results:
We developed five ML models for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved goodresults:
respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96.Conclusions:
ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Blood Chemical Analysis
/
Machine Learning
/
COVID-19 Testing
/
COVID-19
/
Hematologic Tests
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Clin Chem Lab Med
Journal subject:
Chemistry, Clinical
/
Laboratory Techniques and procedures
Year:
2020
Document Type:
Article
Affiliation country:
Cclm-2020-1294
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