用深度学习分析专家决策，更真实地捕捉其不确定性
Utilising Deep Learning to Elicit Expert Uncertainty
- 用深度模型建模专家实际决策信息，替代传统表格数据
- 在结肠癌风险评估中成功提取专家不确定性分布
- 适合需要量化专家判断的医疗、风控等决策场景
近期工作[14]提出一种利用专家决策记录推断先验分布的方法。尽管该方法在生成专家不确定性方面展现出潜力，但仅在表格数据上验证，可能无法完全反映专家决策所依赖的实际信息。本文展示了分析师如何采用深度学习方法，将[14]中的方法应用于专家实际使用的决策信息。我们综述了能有效建模专家决策行为的深度学习模型，并以结肠癌风险评估为例，详细说明这些模型的应用过程，实现对专家不确定性的精准刻画。
原文摘要 · Abstract (English)
Recent work [ 14 ] has introduced a method for prior elicitation that utilizes records of expert decisions to infer a prior distribution. While this method provides a promising approach to eliciting expert uncertainty, it has only been demonstrated using tabular data, which may not entirely represent the information used by experts to make decisions. In this paper, we demonstrate how analysts can adopt a deep learning approach to utilize the method proposed in [14 ] with the actual information experts use. We provide an overview of deep learning models that can effectively model expert decision-making to elicit distributions that capture expert uncertainty and present an example examining the risk of colon cancer to show in detail how these models can be used.
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