AI Based Cancer Detection Models Using Primary Care Datasets

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dc.contributor.authorRistanoski, Goce
dc.contributor.authorEmery, Jon
dc.contributor.authorMartinez Gutierrez, Javiera
dc.contributor.authorMcCarthy, Damien
dc.contributor.authorAickelin, Uwe
dc.date.accessioned2025-01-20T21:01:25Z
dc.date.available2025-01-20T21:01:25Z
dc.date.issued2022
dc.description.abstractCancer is one of the most common and serious medical conditions with more than 144 000 Australians having been diagnosed with cancer in 2019. The non-specific nature of cancer symptoms and its low prevalence make cancer diagnosis particularly challenging, especially for primary care physicians/General Practitioners (GPs). Ongoing research in cancer diagnosis places a heavy focus on understanding the epidemiology of cancer symptoms. With GPs being the first point of contact for most patients, prediction models using the patient's medical history from primary care data can be a useful decision tool for early cancer detection. Our work both investigates the opportunities to use primary care data, specifically pathology data, for developing such decision tools and tackles the challenges coming from uncertainty in the data such as irregular pathology records. We present opportunities using the results within the frequently ordered full blood count to determine relevance to a future cancer diagnosis. By using several different pathology metrics, we show how we can generate features suitable for AI models that can be used to detect cancer 3 months earlier than current practices. Though the work focuses on patients with lung cancer, the methodology can be adjusted to other types of cancer and other data within the medical records. Our findings demonstrate that even when working with incomplete or obscure patient history, hematological measures contain valuable information that can indicate the potential of cancer diagnosis for up to 8 out of 10 patients. The use of the proposed decision tool presents a way to incorporate pathology data in the current cancer diagnosis practices and to incorporate various pathology tests or other primary care datasets for similar purposes.
dc.fuente.origenWOS
dc.identifier.doi10.12720/jait.13.2.192-197
dc.identifier.issn1798-2340
dc.identifier.urihttps://doi.org/10.12720/jait.13.2.192-197
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92878
dc.identifier.wosidWOS:000884923200013
dc.issue.numero2
dc.language.isoen
dc.pagina.final197
dc.pagina.inicio192
dc.revistaJournal of advances in information technology
dc.rightsacceso restringido
dc.subjectexplainable AI
dc.subjectearly cancer detection
dc.subjectuncertainty in data
dc.subjectfeature generation
dc.subject.ods05 Gender Equality
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa05 Igualdad de género
dc.subject.odspa03 Salud y bienestar
dc.titleAI Based Cancer Detection Models Using Primary Care Datasets
dc.typeartículo
dc.volumen13
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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