publications
2023
- Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule MiningCarla Floricel, Andrew Wentzel, Abdallah Mohamed, and 3 more authorsIEEE Transactions on Visualization and Computer Graphics, 2023
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}, } - DASS Good: Explainable Data Mining of Spatial Cohort DataAndrew Wentzel, Carla Floricel, Guadalupe Canahuate, and 5 more authorsComputer Graphics Forum, 2023
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.
@article{wentzel2023dass, title = {DASS Good: Explainable Data Mining of Spatial Cohort Data}, author = {Wentzel, Andrew and Floricel, Carla and Canahuate, Guadalupe and Naser, Mohamed A and Mohamed, Abdallah S and Fuller, Clifton David and van Dijk, Lisanne and Marai, G Elisabeta}, journal = {Computer Graphics Forum}, year = {2023}, } - MouseScholar: Evaluating an Image+Text Search System for BiocurationJuan Trelles Trabucco, Carla Floricel, Cecilia Arighi, and 4 more authorsInternational Conference on Bioinformatics and Biomedicine, 2023
Biocuration is the process of analyzing biological or biomedical articles to organize biological data into data repositories using taxonomies and ontologies. Due to the ex- panding number of articles and the relatively small number of biocurators, automation is desired to improve the workflow of assessing articles worth curating. As figures convey essential information, automatically integrating images may improve cu- ration. In this work, we instantiate and evaluate a first-in-kind, hybrid image+text document search system for biocuration. The system, MouseScholar, leverages an image modality taxonomy derived in collaboration with biocurators, in addition to figure segmentation, and classifiers components as a back-end and a streamlined front-end interface to search and present document results. We formally evaluated the system with ten biocurators on a mouse genome informatics biocuration dataset and collected feedback. The results demonstrate the benefits of blending text and image information when presenting scientific articles for biocuration.
@article{trelles2023mouse, title = {MouseScholar: Evaluating an Image+Text Search System for Biocuration}, author = {Trabucco, Juan Trelles and Floricel, Carla and Arighi, Cecilia and Shatkay, Hagit and Raciti, Daniela and Ringwald, Martin and Marai, G Elisabeta}, journal = {International Conference on Bioinformatics and Biomedicine}, year = {2023}, }
2022
- Opening Access to Visual Exploration of Audiovisual Digital Biomarkers: an OpenDBM Analytics ToolCarla Floricel, Jacob Epifano, Stephanie Caamano, and 4 more authorsIEEE Visualization in Biomedical AI Workshop, 2022
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}, } - Visual Analysis and Detection of Contrails in Aircraft Engine SimulationsNafiul Nipu, Carla Floricel, Negar Naghashzadeh, and 2 more authorsIEEE Transactions on Visualization and Computer Graphics, 2022
Contrails are condensation trails generated from emitted particles by aircraft engines, which perturb Earth’s radiation budget. Simulation modeling is used to interpret the formation and development of contrails. These simulations are computationally intensive and rely on high-performance computing solutions, and the contrail structures are not well defined. We propose a visual computing system to assist in defining contrails and their characteristics, as well as in the analysis of parameters for computer-generated aircraft engine simulations. The back-end of our system leverages a contrail-formation criterion and clustering methods to detect contrails’ shape and evolution and identify similar simulation runs. The front-end system helps analyze contrails and their parameters across multiple simulation runs. The evaluation with domain experts shows this approach successfully aids in contrail data investigation.
@article{nipu2022visual, title = {Visual Analysis and Detection of Contrails in Aircraft Engine Simulations}, author = {Nipu, Nafiul and Floricel, Carla and Naghashzadeh, Negar and Paoli, Roberto and Marai, G Elisabeta}, journal = {IEEE Transactions on Visualization and Computer Graphics}, year = {2022}, }
2021
- Thalis: Human-Machine Analysis of Longitudinal Symptoms in Cancer TherapyCarla Floricel, Nafiul Nipu, Mikayla Biggs, and 6 more authorsIEEE Transactions on Visualization and Computer Graphics, 2021
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}, } - Identifying Symptom Clusters Through Association Rule MiningMikayla Biggs, Carla Floricel, Lisanne Van Dijk, and 5 more authorsArtificial Intelligence in Medicine, 2021
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.
@article{biggs2021identifying, title = {Identifying Symptom Clusters Through Association Rule Mining}, author = {Biggs, Mikayla and Floricel, Carla and Van Dijk, Lisanne and Mohamed, Abdallah SR and David Fuller, C and Marai, G Elisabeta and Zhang, Xinhua and Canahuate, Guadalupe}, journal = {Artificial Intelligence in Medicine}, year = {2021}, } - Parameter Analysis and Contrail Detection of Aircraft Engine SimulationsNafiul Nipu, Carla Floricel, Negar Naghashzadeh, and 2 more authorsIn IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV), 2021
@inproceedings{nipu2021parameter, title = {Parameter Analysis and Contrail Detection of Aircraft Engine Simulations}, author = {Nipu, Nafiul and Floricel, Carla and Naghashzadeh, Negar and Paoli, Roberto and Marai, G Elisabeta}, booktitle = {IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV)}, year = {2021}, organization = {IEEE}, }
2020
- Visualizing Symptom Development During Head and Neck Cancer TreatmentCarla Floricel, Andrew Wentzel, Md Nafiul Nipu, and 5 more authors2020
Approximately 100,000 cases of Head and Neck Cancer (HNC) are diagnosed in the US annually. Patients are increasingly likely to survive, but often experience acute and long term side effects [1]. Hence, great importance has been placed by clinicians on improving patient’s quality of life (QoL) and reducing symptom burden during treatment. We introduce an interactive system which enables clinical and computational experts to visualize and assess medical data. Using novel combinations of visual encodings, our system provides context for new patients based on patients with similar features and symptom evolution, which could help oncologists to create better treatment plans.
@article{floricelvisualizing, title = {Visualizing Symptom Development During Head and Neck Cancer Treatment}, author = {Floricel, Carla and Wentzel, Andrew and Nipu, Md Nafiul and Kumar, Naveen and Canahuate, Guadalupe and Van Dijk, Lisanne and Fuller, C David and Marai, G Elisabeta}, booktitle = {IEEE Transactions on Visualization and Computer Graphics, Poster Session}, year = {2020}, }