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Personalized head and neck cancer therapeutics have greatly improved survivalrates for patients, but are often leading to understudied long-lasting symptomswhich affect quality of life. Sequential rule mining (SRM) is a promisingunsupervised machine learning method for predicting longitudinal patterns intemporal data which, however, can output many repetitive patterns that aredifficult to interpret without the assistance of visual analytics. We present adata-driven, human-machine analysis visual system developed in collaborationwith SRM model builders in cancer symptom research, which facilitatesmechanistic knowledge discovery in large scale, multivariate cohort symptomdata. Our system supports multivariate predictive modeling of post-treatmentsymptoms based on during-treatment symptoms. It supports this goal through anSRM, clustering, and aggregation back end, and a custom front end to helpdevelop and tune the predictive models. The system also explains the resultingpredictions in the context of therapeutic decisions typical in personalizedcare delivery. We evaluate the resulting models and system with aninterdisciplinary group of modelers and head and neck oncology researchers. Theresults demonstrate that our system effectively supports clinical and symptomresearch.

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining