Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining
Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining
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DOI：
10.1109/tvcg.2023.3326939
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发表时间：
2024-01-01
影响因子：
5.2
通讯作者：
Marai，G. Elisabeta
中科院分区：
文献类型：
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作者：
Floricel，Carla;Wentzel，Andrew;Marai，G. Elisabeta
个性化的头颈部癌症疗法极大地提高了患者的存活率，但往往会导致对影响生活质量的长期症状研究不足。序列规则挖掘(SRM)是一种很有前途的无监督机器学习方法，用于预测时态数据中的纵向模式，但它会产生许多重复模式，这些重复模式在没有视觉分析的情况下很难解释。我们提出了一个与癌症症状研究中SRM模型构建者合作开发的数据驱动的人机分析可视化系统，该系统有助于在大规模、多变量队列症状数据中进行机械性知识发现。我们的系统支持基于治疗期间症状对治疗后症状进行多变量预测建模。它通过SRM、集群和聚合后端以及定制前端来支持这一目标，以帮助开发和调整预测模型。该系统还解释了在个性化护理提供中典型的治疗决定的背景下产生的预测。我们与模型师和头颈部肿瘤学研究人员组成的跨学科小组一起对所产生的模型和系统进行评估。结果表明，我们的系统有效地支持了临床和症状研究。
Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research.