ArticleComputer graphics forum : journal of the European Association for Computer Graphics2023
DASS Good: Explainable Data Mining of Spatial Cohort Data.
Article in Computer graphics forum : journal of the European Association for Computer Graphics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 12 citations in OpenAlex.
- L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- Advancing Normal Tissue Complication Probability Modeling with Supervised Contrastive Learning for Predicting Osteoradionecrosis.Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing · 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
- 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
- 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
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
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.
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.