用历史病历数据检测手术后异常医疗操作，辅助临床预警。
Conditional outlier detection for clinical alerting
- 基于电子病历中过往患者数据，识别异常管理行为
- 在4486例术后患者中验证，异常程度越高警报率越高
- 专家评估显示误报率较低，适合临床辅助决策
我们开发并评估了一种数据驱动的方法，用于检测电子健康记录（EHR）系统中存储的过往患者案例中的异常（异常）患者管理行为。假设是，相对于过去患者而言异常的管理行为可能源于潜在错误，因此遇到此类情况时值得发出警报。我们利用来自4,486名心脏术后患者的电子病历数据评估了这一假设，并基于专家小组的意见进行分析。结果表明，基于异常的警报机制可实现合理低的误报率，且异常越强，警报率越高。
原文摘要 · Abstract (English)
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) system. Our hypothesis is that patient-management actions that are unusual with respect to past patients may be due to a potential error and that it is worthwhile to raise an alert if such a condition is encountered. We evaluate this hypothesis using data obtained from the electronic health records of 4,486 post-cardiac surgical patients. We base the evaluation on the opinions of a panel of experts. The results support that anomaly-based alerting can have reasonably low false alert rates and that stronger anomalies are correlated with higher alert rates.
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