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