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.

翻译：当数据包含空间信息时（例如跨相邻危及器官的辐射剂量分布），开发具有临床应用价值的机器学习模型极具挑战性。本文描述了DASS建模系统的协同设计过程，该系统支持人机混合开发与验证，用于预测头颈癌患者放射治疗剂量相关的长期毒性。通过与肿瘤学与数据挖掘领域专家协作开发，DASS整合了人在回路的可视化引导、空间数据与可解释人工智能技术，以自动数据挖掘增强领域知识。我们通过开发两种实用临床分层模型来演示DASS，并报告领域专家的反馈。最后，本文总结了本次协作经验中获取的设计启示。