I am a Ph.D. student at the University of Illinois Chicago in Computer Science, doing research in Visual Data Science at the Electronic Visualization Laboratory. I finished my Bachelors at the Politehnica University of Bucharest, in Romania.
I am interested in helping people understand complex data through visualization and I enjoy designing novel visualizations for large-scale temporal trends and cohort data. I am focused on designing, developing, and evaluating Visual Analytics Systems, primarily for medical data.
My hobbies include reading, traveling, biking, and cooking.
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.
@article{floricel2023roses,title={Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining},author={Floricel, Carla and Wentzel, Andrew and Mohamed, Abdallah and Fuller, C David and Canahuate, Guadalupe and Marai, G Elisabeta},journal={IEEE Transactions on Visualization and Computer Graphics},year={2023},}
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.
@article{floricel2022opening,title={Opening Access to Visual Exploration of Audiovisual Digital Biomarkers: an OpenDBM Analytics Tool},author={Floricel, Carla and Epifano, Jacob and Caamano, Stephanie and Kark, Sarah and Christie, Rich and Masino, Aaron and Paredes, Andre D},journal={IEEE Visualization in Biomedical AI Workshop},year={2022},}
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.
@article{floricel2021thalis,title={Thalis: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy},author={Floricel, Carla and Nipu, Nafiul and Biggs, Mikayla and Wentzel, Andrew and Canahuate, Guadalupe and Van Dijk, Lisanne and Mohamed, Abdallah and Fuller, C David and Marai, G Elisabeta},journal={IEEE Transactions on Visualization and Computer Graphics},year={2021},}
