We present an interactive analysis system to support sequential rule mining (SRM) model builders who work in cancer symptom research. 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. Our system facilitates mechanistic knowledge discovery of treatment-related toxicities (i.e., "roses have thorns") in large-scale cohort data.

This work was developed by visual computing researchers at EVL in collaboration with data mining specialists at the University of Iowa, and clinicians at the MD Anderson Cancer Center in Texas, and it is supported by funding from the US National Institutes of Health (NIH awards NCI-R01-CA258827 and NLM-R01-LM012527), and US National Science Foundation (NSF awards CDSE-1854815 and CNS-1828265).