Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population

Banerjee, A., Ray, S. , Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., Baker, M. and Mackenzie, L. S. (2020) Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population. International Immunopharmacology, 86, 106705. (doi: 10.1016/j.intimp.2020.106705) (PMCID:PMC7296324)

[img]
Preview
Text
217969.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

936kB

Abstract

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic Covid 19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning, an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood count results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 93-94%) and those not admitted to hospital or in the community (AUC = 80-86%). Here AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood count can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited.

Item Type:Articles
Keywords:SARS-CoV-2, machine learning, artificial neural network (ANN), screening, full blood count, leukocytes, monocytes.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ray, Professor Surajit
Authors: Banerjee, A., Ray, S., Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., Baker, M., and Mackenzie, L. S.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:International Immunopharmacology
Publisher:Elsevier
ISSN:1567-5769
ISSN (Online):1878-1705
Published Online:16 June 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in International Immunopharmacology 86: 106705
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record