Download PDFOpen PDF in browserUsing Active Learning to Improve the Treatment Selection on Pancreatic Cancer Patients3 pages•Published: February 16, 2023AbstractThe use of Machine Learning (ML) techniques in the context of Cancer prognosis, di- agnosis and treatment is nowadays a reality. Some types of cancers could greatly benefit from specific techniques that are designed to work in a scarcity of data scenarios, or when obtaining labeled data is a time-consuming and/or costly task. It is the case of the Pan- creatic Adenocarcinoma. We present an experiment where Active Learning (AL) is used as the basis to create a model which performs a classification task where a human expert (in this experiment, a medical doctor) needs to determine whether a pancreatic cancer patient must be treated with chemotherapy, not treated, or he/she is unsure about the therapy. The use of AL techniques allows us to improve the accuracy of the model, and the inclusion of expert opinions may help us in the future to add explanatory capabilities to the system.Keyphrases: active learning, human in the loop machine learning, pancreatic cancer In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 70-72.
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