  Although cancer patients survive years after oncologic therapy, they are
plagued with long-lasting or permanent residual symptoms, whose severity, rate
of development, and resolution after treatment vary largely between survivors.
The analysis and interpretation of symptoms is complicated by their partial
co-occurrence, variability across populations and across time, and, in the case
of cancers that use radiotherapy, by further symptom dependency on the tumor
location and prescribed treatment. We describe THALIS, an environment for
visual analysis and knowledge discovery from cancer therapy symptom data,
developed in close collaboration with oncology experts. Our approach leverages
unsupervised machine learning methodology over cohorts of patients, and, in
conjunction with custom visual encodings and interactions, provides context for
new patients based on patients with similar diagnostic features and symptom
evolution. We evaluate this approach on data collected from a cohort of head
and neck cancer patients. Feedback from our clinician collaborators indicates
that THALIS supports knowledge discovery beyond the limits of machines or
humans alone, and that it serves as a valuable tool in both the clinic and
symptom research.
