Prediction of SARS-CoV-2-positivity from million-scale complete blood counts using machine learning.

ABSTRACT:

Background

The Complete Blood Count (CBC) is a commonly used low-cost test that measures white blood cells, red blood cells, and platelets in a person's blood. It is a useful tool to support medical decisions, as intrinsic variations of each analyte bring relevant insights regarding potential diseases. In this study, we aimed at developing machine learning models for COVID-19 diagnosis through CBCs, unlocking the predictive power of non-linear relationships between multiple blood analytes.

Methods

We collected 809,254 CBCs and 1,088,385 RT-PCR tests for SARS-Cov-2, of which 21% (234,466) were positive, from 900,220 unique individuals. To properly screen COVID-19, we also collected 120,807 CBCs of 16,940 individuals who tested positive for other respiratory viruses. We propos

SUBMITTER: Zuin G 

PROVIDER: S-EPMC9199341 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature