Explainability Design Patterns in Clinical Decision Support Systems
Explainability Design Patterns in Clinical Decision Support Systems
复制标题
临床决策支持系统中的可解释性设计模式
DOI：
10.1007/978-3-030-50316-1_45
复制
发表时间：
2020
期刊：
影响因子：
2.4
通讯作者：
Mohammad Naiseh
中科院分区：
文献类型：
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作者：
Mohammad Naiseh
． 本文报告了正在进行的博士项目在解释临床决策支持系统（cdss）的建议，以医疗从业者。最近，医学领域的可解释性研究取得了突飞猛进的进展，主要集中在两种方法上：第一种方法侧重于开发本质上可解释和透明的模型（例如基于规则的算法）。第二部分研究了黑箱模型的可解释性，而没有将其背后的机制（例如LIME）视为事后解释。然而，忽略了人为因素和可用性方面的解释，在系统建议之后引入了新的风险，例如过度信任和信任不足。由于这种限制，对cdss的可用解释的需求越来越大，以便通过确定建议何时是正确的，从而在这些系统中集成信任校准和知情决策。本研究旨在发展可解释性设计模式，以校准医师对cdss的信任。本文总结了博士论文的研究方法，并围绕研究问题进行了文献讨论。
. This paper reports on the ongoing PhD project in the field of explaining the clinical decision support systems (CDSSs) recommendations to medical practitioners. Recently, the explainability research in the medical domain has witnessed a surge of advances with a focus on two main methods: The first focuses on developing models that are ex-plainable and transparent in its nature (e.g. rule-based algorithms). The second investigates the interpretability of the black-box models without looking at the mechanism behind it (e.g. LIME) as a post-hoc explanation. However, overlooking the human-factors and the usability aspect of the explanation introduced new risks following the system recommendations, e.g. over-trust and under-trust. Due to such limitation, there is a growing demand for usable explanations for CDSSs to enable the integration of trust calibration and informed decision-making in these systems by identifying when the recommendation is correct to follow. This research aims to develop explainability design patterns with the aim of calibrating medical practitioners trust in the CDSSs. This paper concludes the PhD methodology and literature around the research problem is also discussed.