Background

Decision making in the Emergency Department (ED) requires timely identification of clinical information relevant to the complaints. Existing information retrieval solutions for the electronic health record (EHR) focus on patient cohort identification and lack clinical relevancy ranking. We aimed to compare knowledge-based (KB) and unsupervised statistical methods for ranking EHR information by relevancy to a chief complaint of chest or back pain among ED patients.

Methods

We used Pointwise-mutual information (PMI) with corpus level significance adjustment (cPMId), which modifies PMI to reward co-occurrence patterns with a higher absolute count. cPMId for each pair of medication/problem and chief complaint was estimated from a corpus of 100,000 un-annotated ED encounters. Five specialist physicians ranked the relevancy of medications and problems to each chief complaint on a 0–4 Likert scale to form the KB ranking. Reverse chronological order was used as a baseline. We directly compared the three methods on 1010 medications and 2913 problems from 99 patients with chest or back pain, where each item was manually labeled as relevant or not to the chief complaint, using mean average-precision.

Results

cPMId out-performed KB ranking on problems (86.8% vs. 81.3%, p < 0.01) but under-performed it on medications (93.1% vs. 96.8%, p < 0.01). Both methods significantly outperformed the baseline for both medications and problems (71.8% and 72.1%, respectively, p < 0.01 for both comparisons). The two complaints represented virtually completely different information needs (average Jaccard index of 0.008).

Conclusion

A fully unsupervised statistical method can provide a reasonably accurate, low-effort and scalable means for situation-specific ranking of clinical information within the EHR.

中文翻译：

---

结构化病历的无监督临床相关性排序以检索急诊科的特定条件信息

背景

急诊科 (ED) 的决策需要及时识别与投诉相关的临床信息。现有的电子健康记录 (EHR) 信息检索解决方案侧重于患者队列识别，缺乏临床相关性排名。我们旨在比较基于知识 (KB) 和无监督统计方法，通过与 ED 患者胸痛或背痛主诉的相关性对 EHR 信息进行排序。

方法

我们使用具有语料库级别显着性调整 (cPMId) 的逐点互信息 (PMI)，它修改 PMI 以奖励具有更高绝对计数的共现模式。每对药物/问题和主诉的 cPMId 是从 100,000 次未注释的 ED 遭遇的语料库中估计出来的。五名专科医生按照 0-4 Likert 量表对药物和问题与每个主诉的相关性进行排名，以形成 KB 排名。反向时间顺序用作基线。我们直接比较了来自 99 名胸痛或背痛患者的 1010 种药物和 2913 个问题的三种方法，其中每个项目都使用平均平均精度手动标记为与主诉相关或不相关。

结果

cPMId 在问题上的表现优于 KB 排名（86.8% 对 81.3%，p < 0.01），但在药物方面表现不佳（93.1% 对 96.8%，p < 0.01）。两种方法在药物治疗和问题方面的表现均显着优于基线（分别为 71.8% 和 72.1%，两种比较的p < 0.01）。这两个投诉代表了几乎完全不同的信息需求（平均 Jaccard 指数为 0.008）。

结论

完全无监督的统计方法可以为 EHR 内临床信息的特定情况排序提供合理准确、省力且可扩展的方法。