6 research outputs found
Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining
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
Opening Access to Visual Exploration of Audiovisual Digital Biomarkers: an OpenDBM Analytics Tool
Digital biomarkers (DBMs) are a growing field and increasingly tested in the
therapeutic areas of psychiatric and neurodegenerative disorders. Meanwhile,
isolated silos of knowledge of audiovisual DBMs use in industry, academia, and
clinics hinder their widespread adoption in clinical research. How can we help
these non-technical domain experts to explore audiovisual digital biomarkers?
The use of open source software in biomedical research to extract patient
behavior changes is growing and inspiring a shift toward accessibility to
address this problem. OpenDBM integrates several popular audio and visual open
source behavior extraction toolkits. We present a visual analysis tool as an
extension of the growing open source software, OpenDBM, to promote the adoption
of audiovisual DBMs in basic and applied research. Our tool illustrates
patterns in behavioral data while supporting interactive visual analysis of any
subset of derived or raw DBM variables extracted through OpenDBM.Comment: 6 pages, 2 figures, 2022 IEEE VIS Workshop - Visualization in
BioMedical A
DASS Good: Explainable Data Mining of Spatial Cohort Data
Developing applicable clinical machine learning models is a difficult task
when the data includes spatial information, for example, radiation dose
distributions across adjacent organs at risk. We describe the co-design of a
modeling system, DASS, to support the hybrid human-machine development and
validation of predictive models for estimating long-term toxicities related to
radiotherapy doses in head and neck cancer patients. Developed in collaboration
with domain experts in oncology and data mining, DASS incorporates
human-in-the-loop visual steering, spatial data, and explainable AI to augment
domain knowledge with automatic data mining. We demonstrate DASS with the
development of two practical clinical stratification models and report feedback
from domain experts. Finally, we describe the design lessons learned from this
collaborative experience.Comment: 10 pages, 9 figure
Identifying Symptom Clusters Through Association Rule Mining
Cancer patients experience many symptoms throughout their cancer treatment and sometimes suffer from lasting effects post-treatment. Patient-Reported Outcome (PRO) surveys provide a means for monitoring the patient's symptoms during and after treatment. Symptom cluster (SC) research seeks to understand these symptoms and their relationships to define new treatment and disease management methods to improve patient's quality of life. This paper introduces association rule mining (ARM) as a novel alternative for identifying symptom clusters. We compare the results to prior research and find that while some of the SCs are similar, ARM uncovers more nuanced relationships between symptoms such as anchor symptoms that serve as connections between interference and cancer-specific symptoms
Thalis: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy
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