ArticleIEEE transactions on visualization and computer graphics2022
THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.
Article in IEEE transactions on visualization and computer graphics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Applications of human-machine collaborative decision-making: A review of research with recent developments.iScience · 2026Review
- Improving Survival Prediction of Head and Neck Cancer Patients by Modeling Long-term Symptom Burden.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator.... IEEE Workshop on Visual Analytics in Healthcare. IEEE Workshop on Visual Analytics in Healthcare · 2025Article
- FlavorCharter: Development of a Smartphone App for Quantifying Food Flavor.Proceedings ... ICDM workshops. IEEE International Conference on Data Mining · 2025Article
- mEnergy: Development of a Novel mHealth Multi-device Platform for Near Real-time Remote Assessment of Energy Disorders.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Deep Learning Approaches to Forecast Physical and Mental Deterioration During Chemotherapy in Patients with Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- PRO-Based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer Patients.IEEE journal of biomedical and health informatics · 2025Article
- DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer.IEEE transactions on visualization and computer graphics · 2025Article
- AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management.Bioengineering (Basel, Switzerland) · 2024Article
- TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining.IEEE transactions on visualization and computer graphics · 2024Article
- DASS Good: Explainable Data Mining of Spatial Cohort Data.Computer graphics forum : journal of the European Association for Computer Graphics · 2023Article
- Visual Analysis and Detection of Contrails in Aircraft Engine Simulations.IEEE transactions on visualization and computer graphics · 2023Article
- Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool.Frontiers in oncology · 2023Article
- A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care.IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.