医学表格数据分类与风险预测的不确定性估计
Uncertainty estimation for classification and risk prediction on medical tabular data
摘要：
在医疗保健等数据稀缺的领域，模型通常会对罕见疾病的患者进行预测，测量模型预测的不确定性的能力可能会提高决策支持工具的有效性并增加用户的信任。这项工作推进了医疗表格数据的分类和风险预测的不确定性估计的理解，在一个双重的方式。首先，我们扩展和完善了一套诊断学，以选择一种不确定性估计技术，引入临床相关场景的测试，如推广到不常见的病理，临床协议的变化和模拟损坏的数据。我们还根据临床用例对这些药物进行了区分。其次，我们观察到，合奏和相关技术表现不佳，当涉及到检测域外的例子，一个关键的任务，这是更成功地进行自动编码器。这些评论丰富的考虑类不平衡，建模后校准和其他建模程序的不确定性估计的相互作用。我们的研究结果得到了一系列玩具和现实数据实验的支持。
英文摘要：
In a data-scarce field such as healthcare, where models often deliver predictions on patients with rare conditions, the ability to measure the uncertainty of a model's prediction could potentially lead to improved effectiveness of decision support tools and increased user trust. This work advances the understanding of uncertainty estimation for classification and risk prediction on medical tabular data, in a two-fold way. First, we expand and refine the set of heuristics to select an uncertainty estimation technique, introducing tests for clinically-relevant scenarios such as generalization to uncommon pathologies, changes in clinical protocol and simulations of corrupted data. We furthermore differentiate these heuristics depending on the clinical use-case. Second, we observe that ensembles and related techniques perform poorly when it comes to detecting out-of-domain examples, a critical task which is carried out more successfully by auto-encoders. These remarks are enriched by considerations of the interplay of uncertainty estimation with class imbalance, post-modeling calibration and other modeling procedures. Our findings are supported by an array of experiments on toy and real-world data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
