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.Comment: 10 pages, 9 figure