用软调和函数检测医疗数据异常标签，提升临床警报准确性。
Conditional anomaly detection using soft harmonic functions: An application to clinical alerting
- 基于软调和函数的非参数方法，估计标签置信度
- 在真实电子病历数据上显著识别异常标签
- 适合医疗系统中的异常事件监测与预警
及时发现临床中的异常事件至关重要。本文研究条件异常检测问题，旨在识别响应异常的数据实例，如重要检验项目的遗漏。提出一种基于软调和解的新非参数方法，用于估计标签置信度以检测异常误标。通过正则化避免孤立点及分布边界处的误检。在真实世界电子健康记录数据集上验证了该方法的有效性，并与多个基线方法进行了对比。
原文摘要 · Abstract (English)
Timely detection of concerning events is an important problem in clinical practice. In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response, such as the omission of an important lab test. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method in detecting unusual labels on a real-world electronic health record dataset and compare it to several baseline approaches.
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