# DASS Good: Explainable Data Mining of Spatial Cohort Data

arXiv: https://arxiv.org/abs/2304.04870 · HTML: https://mlsanity.com/paper/2304.04870/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 83 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models](https://mlsanity.com/paper/2008.08353/) (2020)
- [How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?](https://mlsanity.com/paper/1909.03486/) (2019)
- [Clustrophile 2: Guided Visual Clustering Analysis](https://mlsanity.com/paper/1804.03048/) (2018)
- [VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models](https://mlsanity.com/paper/2108.02550/) (2021)
- [Illusion of Causality in Visualized Data](https://mlsanity.com/paper/1908.00215/) (2019)
- [An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets](https://mlsanity.com/paper/2109.09160/) (2021)
- [Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration](https://mlsanity.com/paper/1907.05919/) (2019)
- [Explainable Spatial Clustering: Leveraging Spatial Data in Radiation Oncology](https://mlsanity.com/paper/2008.11282/) (2020)
- [THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy](https://mlsanity.com/paper/2108.02817/) (2021)
- [Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data](https://mlsanity.com/paper/2110.04875/) (2021)

## Abstract (arXiv)

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.