Using Active Learning to Improve the Treatment Selection on Pancreatic Cancer Patients

Abstract

The 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.

Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer Treatment

“…In this research, we continued from our previous work on AL Bobes-Bascarán et al (2021) Bobes-Bascarán et al (2023, where we first experimented with generated synthetic data and an Active Learning approach, and then followed with a real dataset introducing medical doctors in the loop of a therapy selection model for pancreatic cancer. On our previous work the goal was to overcame the scarcity of data available on the Pancreatic Cancer context by incorporating humans into the ML loop Mosqueira-Rey et al (2022b).…”