用概率模型识别术后心脏病患管理决策中的异常行为。
Evidence-based anomaly detection in clinical domains
- 基于贝叶斯网络构建患者决策概率模型
- 可定位与相似病情患者差异显著的管理决策
- 适用于临床决策质量评估与医疗安全监控
异常检测方法在识别值得关注事件方面具有重要作用。本文开发并检验了新的概率异常检测方法，用于评估特定患者的管理决策，并识别出与相同或类似病情患者相比高度异常的决策。所用统计量来源于从历史患者病例数据库中学习得到的贝叶斯网络等概率模型。我们将该方法应用于识别术后心脏手术患者中不寻常的管理决策问题。
原文摘要 · Abstract (English)
Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic anomaly detection methods that let us evaluate management decisions for a specific patient and identify those decisions that are highly unusual with respect to patients with the same or similar condition. The statistics used in this detection are derived from probabilistic models such as Bayesian networks that are learned from a database of past patient cases. We apply our methods to the problem of identifying unusual patient-management decisions in post-surgical cardiac patients.
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