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ItemAI Based Cancer Detection Models Using Primary Care DatasetsRistanoski, G ; Emery, J ; Martinez Gutierrez, J ; McCarthy, D ; Aickelin, U (ENGINEERING & TECHNOLOGY PUBLISHING, 2022-04-01)Cancer 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.
ItemHandling uncertainty using features from pathology: Opportunities in primary care data for developing high risk cancer survival methodsRistanoski, G ; Emery, J ; Martinez Gutierrez, J ; McCarthy, D ; Aickelin, U (ACM, 2021)More than 144 000 Australians were diagnosed with cancer in 2019. Diagnosing cancer in primary care is challenging due to the non-specific nature of cancer symptoms and its low prevalence. Understanding the epidemiology of cancer symptoms and patterns of presentation in patient's medical history from primary care data could be important to improve earlier detection and cancer outcomes. As past medical data about a patient can be incomplete, irregular or missing, this creates additional challenges when attempting to use the patient's history for any new diagnosis. Our research aims to investigate the opportunities in a patient's pathology history available to a GP, initially focused on the results within the frequently ordered full blood count to determine relevance to a future high-risk cancer prognosis, and treatment outcome. We investigated how past pathology test results can lead to deriving features that can be used to predict cancer outcomes, with emphasis on patients at risk of not surviving the cancer within 2-year period. This initial work focuses on patients with lung cancer, although the methodology can be applied to other types of cancer and other data within the medical record. Our findings indicate that even in cases of incomplete or obscure patient history, hematological measures can be useful in generating features relevant for predicting cancer risk and survival. The results strongly indicate to add the use of pathology test data for potential high-risk cancer diagnosis, and utilize additional pathology metrics or other primary care datasets even more for similar purposes.