Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios.

ABSTRACT:

Background

COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity.

Purpose

This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results.

Methods

Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT

SUBMITTER: Avila E 

PROVIDER: S-EPMC7331623 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature