Classification of Long COVID from General Practitioner Diagnosis Text

Abbish Kamalakkannan; Gorkem Sezgin; Adam McLeod; Christopher Pearce; Andrew Georgiou; Mirela Prgomet; Stephen Weeding

doi:10.3233/SHTI230387

Classification of Long COVID from General Practitioner Diagnosis Text

Stud Health Technol Inform. 2023 Jun 22:304:124-125. doi: 10.3233/SHTI230387.

Authors

Abbish Kamalakkannan¹, Gorkem Sezgin¹, Adam McLeod², Christopher Pearce², Andrew Georgiou¹, Mirela Prgomet¹, Stephen Weeding³

Affiliations

¹ Australian Institute of Health Innovation, Macquarie University, Australia.
² Outcome Health, Victoria, Australia.
³ Eastern Melbourne Primary Health Network, Victoria, Australia.

PMID: 37347585
DOI: 10.3233/SHTI230387

Abstract

Major challenges exist in identifying Long COVID patients from diagnosis texts recorded by general practitioners. A classification framework is proposed that can be used to identify Long COVID patients given these unstructured diagnostic texts. This framework can be leveraged to provide a general understanding of the risk factors, management strategies, and outcomes associated with Long COVID in Australia.

Keywords: COVID-19; diagnosis text classification; long COVID; post COVID syndrome.

MeSH terms

Australia
COVID-19 Testing
COVID-19* / diagnosis
General Practitioners*
Humans
Post-Acute COVID-19 Syndrome
Records