用历史病历数据检测手术后异常诊疗决策，提升医疗预警准确率。
Outlier detection for patient monitoring and alerting
- 基于电子病历中的历史病例，识别与常规诊疗不符的异常决策。
- 对222条警报评估显示，异常程度越强，真实警报率越高，达66%。
- 适合临床医生和医疗系统开发者用于改进患者监护预警机制。
我们开发并评估了一种基于数据的方法，用于检测电子健康记录（EHR）中术后患者管理决策的异常情况。假设认为，与既往患者护理模式显著不同的决策可能存在错误，值得发出警报。研究使用4486名心脏手术后患者的EHR数据及其中产生的222条警报进行评估，基于专家小组的意见判断。结果支持该假设：基于异常值的警报系统可实现较高的真实警报率。针对不同患者管理行为，真实警报率在25%至66%之间，其中最强异常对应的警报率达66%。
原文摘要 · Abstract (English)
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25\% to 66\% for a variety of patient-management actions, with 66\% corresponding to the strongest outliers.
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