ArXiv Preprint
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.
Aditya Bhattacharya, Jeroen Ooge, Gregor Stiglic, Katrien Verbert
2023-02-21
