DASS Good: Explainable Data Mining of Spatial Cohort Data

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dc.contributor.authorWentzel, Andrewen_US
dc.contributor.authorFloricel, Carlaen_US
dc.contributor.authorCanahuate, Guadalupeen_US
dc.contributor.authorNaser, Mohamed A.en_US
dc.contributor.authorMohamed, Abdallah S.en_US
dc.contributor.authorFuller, Clifton Daviden_US
dc.contributor.authorDijk, Lisanne vanen_US
dc.contributor.authorMarai, G. Elisabetaen_US
dc.contributor.editorBujack, Roxanaen_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorSchreck, Tobiasen_US
dc.date.accessioned2023-06-10T06:16:53Z
dc.date.available2023-06-10T06:16:53Z
dc.date.issued2023
dc.description.abstractDeveloping 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.en_US
dc.description.number3
dc.description.sectionheadersVisualization for Life Sciences
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14830
dc.identifier.issn1467-8659
dc.identifier.pages283-295
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14830
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14830
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectCCS Concepts: Human-centered computing -> Scientific visualization; Computing methodologies -> Machine learning; Applied computing -> Life and medical sciences
dc.subjectHuman centered computing
dc.subjectScientific visualization
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.subjectApplied computing
dc.subjectLife and medical sciences
dc.titleDASS Good: Explainable Data Mining of Spatial Cohort Dataen_US
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