面向临床警报的条件式异常检测 / Conditional outlier detection for clinical alerting
1️⃣ 一句话总结
本文提出一种利用电子病历数据自动识别临床异常操作的方法——当医生对患者的处理方式与过去相似病例显著不同时，系统自动发出警报以提示潜在错误，并通过专家评估验证了该方法能有效控制误报率，异常越强烈警报准确率越高。
arXiv 提交日期: 2026-05-06
Abstract - Conditional outlier detection for clinical alerting
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.
源自 arXiv: 2605.05124