DASS Good: Explainable Data Mining of Oncology Imaging and Toxicity Data.

Andrew Wentzel, Carla Gabriela Floricel, Guadalupe Canahuate, Mohamed A Naser, Abdallah Mohamed, Clifton David Fuller, Lisanne van Dijk, and G. Elisabeta Marai https://diglib.eg.org/handle/10.1111/cgf14830 Abstract: Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.