基于条件异常检测，精准识别患者管理中的异常决策与检验行为
Conditional anomaly detection methods for patient-management alert systems
- 通过属性间条件依赖关系，定位特定条件下异常数据点
- 在真实医疗数据中实现对异常入院决策和检验订单的高准确识别
- 适用于临床预警系统，帮助医生及时发现危重患者异常风险
异常检测方法可有效识别数据中的异常或有趣模式。近期提出的条件异常检测框架将异常检测拓展至识别数据中部分属性上的异常模式，且异常始终依赖于其余属性的取值。本文聚焦基于实例的条件异常检测方法，利用距离度量识别对异常检测最关键的样本。研究了多种度量及度量学习方法以优化性能。在两个真实世界问题上验证了该方法的有效性：一是识别社区获得性肺炎患者的异常入院决策，二是检测用于确认肝素诱导血小板减少症（HIT）的HPF4检测异常订单。该病症由肝素治疗引发，具有致命风险。
原文摘要 · Abstract (English)
Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance-based methods for detecting conditional anomalies. The methods rely on the distance metric to identify examples in the dataset that are most critical for detecting the anomaly. We investigate various metrics and metric learning methods to optimize the performance of the instance-based anomaly detection methods. We show the benefits of the instance-based methods on two real-world detection problems: detection of unusual admission decisions for patients with the community-acquired pneumonia and detection of unusual orders of an HPF4 test that is used to confirm Heparin induced thrombocytopenia - a life-threatening condition caused by the Heparin therapy.
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