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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | No |
| Status | Published in final form |
| Language |
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| ISBN | 978-3-03868-219-6 |
| Page Range | 13 - 17 |
| Event Title | EuroVis 2023 - Short Papers |
| Event Type | workshop |
| Event Location | Leipzig |
| Event Start Date | June 14 - 2023 |
| Event End Date | June 14 - 2023 |
| Series Name | EuroVis 2023 - Short Papers |
| Publisher | The Eurographics Association |
| Abstract Text | Many real-world machine learning workflows exist in longitudinal, interactive machine learning (ML) settings. This longitudinal nature is often due to incremental increasing of data, e.g., in clinical settings, where observations about patients evolve over their care period. Additionally, experts may become a bottleneck in the workflow, as their limited availability, combined with their role as human oracles, often leads to a lack of ground truth data. In such cases where ground truth data is small, the validation of interactive machine learning workflows relies on domain experts. Only those humans can assess the validity of a model prediction, especially in new situations that have been covered only weakly by available training data. Based on our experiences working with domain experts of a pediatric hospital's intensive care unit, we derive requirements for the design of support interfaces for the validation of interactive ML workflows in fast-paced, high-intensity environments. We present RiskFix, a software package optimized for the validation workflow of domain experts of such contexts. RiskFix is adapted to the cognitive resources and needs of domain experts in validating and giving feedback to the model. Also, RiskFix supports data scientists in their model-building work, with appropriate data structuring for the re-calibration (and possible retraining) of ML models. |
| Free access at | DOI |
| Digital Object Identifier | 10.2312/evs.20231036 |
| Other Identification Number | merlin-id:24325 |
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| Keywords | Interactive Machine Learning, Visual Analytics, Healthcare, Human Model Validation |