Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations
| Autor: | Aditya Bhattacharya, Jeroen Ooge, Gregor Stiglic, Katrien Verbert |
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| Rok vydání: | 2023 |
| Předmět: |
Software Engineering (cs.SE)
FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Software Engineering Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction Human-Computer Interaction (cs.HC) Machine Learning (cs.LG) |
| Zdroj: | Proceedings of the 28th International Conference on Intelligent User Interfaces. |
| Popis: | Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare. However, little research has compared the utility of different explanation methods in guiding healthcare experts for patient care. Moreover, it is unclear how useful, understandable, actionable and trustworthy these methods are for healthcare experts, as they often require technical ML knowledge. This paper presents an explanation dashboard that predicts the risk of diabetes onset and explains those predictions with data-centric, feature-importance, and example-based explanations. We designed an interactive dashboard to assist healthcare experts, such as nurses and physicians, in monitoring the risk of diabetes onset and recommending measures to minimize risk. We conducted a qualitative study with 11 healthcare experts and a mixed-methods study with 45 healthcare experts and 51 diabetic patients to compare the different explanation methods in our dashboard in terms of understandability, usefulness, actionability, and trust. Results indicate that our participants preferred our representation of data-centric explanations that provide local explanations with a global overview over other methods. Therefore, this paper highlights the importance of visually directive data-centric explanation method for assisting healthcare experts to gain actionable insights from patient health records. Furthermore, we share our design implications for tailoring the visual representation of different explanation methods for healthcare experts. Comment: \c{opyright} Bhattacharya et al, 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. Copyright is held by the owner/author(s). Publication rights licensed to ACM. The definitive version was published in ACM IUI '23: 28th International Conference on Intelligent User Interfaces Proceedings, https://doi.org/10.1145/3581641.3584075 |
| Databáze: | OpenAIRE |
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