A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data

Aim: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). Materials & methods: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patient...

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Bibliografiske detaljer

Principais autores:	Patrick A Gladding, Zina Ayar, Kevin Smith, Prashant Patel, Julia Pearce, Shalini Puwakdandawa, Dianne Tarrant, Jon Atkinson, Elizabeth McChlery, Merit Hanna, Nick Gow, Hasan Bhally, Kerry Read, Prageeth Jayathissa, Jonathan Wallace, Sam Norton, Nick Kasabov, Cristian S Calude, Deborah Steel, Colin Mckenzie
Format:	Artigo
Sprog:	Inglês
Udgivet:	Taylor & Francis Group 2021-08-01
Serier:	Future Science OA
Fag:	biological age COVID-19 full blood count heart failure hematology machine learning
Online adgang:	https://www.future-science.com/doi/10.2144/fsoa-2020-0207
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