DASS Good: Explainable Data Mining of Spatial Cohort Data
DASS Good: Explainable Data Mining of Spatial Cohort Data
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DOI：
10.1111/cgf.14830
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发表时间：
2023-06-01
影响因子：
2.5
通讯作者：
Marai，G. E.
中科院分区：
文献类型：
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作者：
Wentzel，A.;Floricel，C.;Marai，G. E.
当数据包括空间信息时，开发适用的临床机器学习模型是一项艰巨的任务，例如，相邻器官的辐射剂量分布。我们描述了建模系统DASS的协同设计，以支持混合人机开发和预测模型的验证，用于估计头颈部癌症患者与放疗剂量相关的长期毒性。DASS是与肿瘤学和数据挖掘领域专家合作开发的，它结合了人在环视觉转向、空间数据和可解释的人工智能，通过自动数据挖掘来增强领域知识。我们展示了DASS的发展，两个实际的临床分层模型和领域专家的反馈报告。最后，我们描述的设计经验教训，从这种合作的经验。
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.