Wentzel, AndrewFloricel, CarlaCanahuate, GuadalupeNaser, Mohamed A.Mohamed, Abdallah S.Fuller, Clifton DavidDijk, Lisanne vanMarai, G. ElisabetaBujack, RoxanaArchambault, DanielSchreck, Tobias2023-06-102023-06-1020231467-8659https://doi.org/10.1111/cgf.14830https://diglib.eg.org:443/handle/10.1111/cgf14830Developing 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.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing -> Scientific visualization; Computing methodologies -> Machine learning; Applied computing -> Life and medical sciencesHuman centered computingScientific visualizationComputing methodologiesMachine learningApplied computingLife and medical sciences10.1111/cgf.14830283-29513 pages