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Mining clinical phrases from nursing notes to discover risk factors of patient deterioration.
International Journal of Medical Informatics ( IF 3.7 ) Pub Date : 2019-12-14 , DOI: 10.1016/j.ijmedinf.2019.104053
Zfania Tom Korach 1 , Jie Yang 1 , Sarah Collins Rossetti 2 , Kenrick D Cato 3 , Min-Jeoung Kang 1 , Christopher Knaplund 3 , Kumiko O Schnock 1 , Jose P Garcia 4 , Haomiao Jia 3 , Jessica M Schwartz 3 , Li Zhou 1
Affiliation  

OBJECTIVE Early identification and treatment of patient deterioration is crucial to improving clinical outcomes. To act, hospital rapid response (RR) teams often rely on nurses' clinical judgement typically documented narratively in the electronic health record (EHR). We developed a data-driven, unsupervised method to discover potential risk factors of RR events from nursing notes. METHODS We applied multiple natural language processing methods, including language modelling, word embeddings, and two phrase mining methods (TextRank and NC-Value), to identify quality phrases that represent clinical entities from unannotated nursing notes. TextRank was used to determine the important word-sequences in each note. NC-Value was then used to globally rank the locally-important sequences across the whole corpus. We evaluated our method both on its accuracy compared to human judgement and on the ability of the mined phrases to predict a clinical outcome, RR event hazard. RESULTS When applied to 61,740 hospital encounters with 1,067 RR events and 778,955 notes, our method achieved an average precision of 0.590 to 0.764 (when excluding numeric tokens). Time-dependent covariates Cox model using the phrases achieved a concordance index of 0.739. Clustering the phrases revealed clinical concepts significantly associated with RR event hazard. DISCUSSION Our findings demonstrate that our minimal-annotation, unsurprised method can rapidly mine quality phrases from a large amount of nursing notes, and these identified phrases are useful for downstream tasks, such as clinical outcome predication and risk factor identification.

中文翻译:


从护理笔记中挖掘临床短语,发现患者病情恶化的风险因素。



目的 早期识别和治疗患者病情恶化对于改善临床结果至关重要。为了采取行动,医院快速反应 (RR) 团队通常依赖护士的临床判断,这些判断通常以叙述方式记录在电子健康记录 (EHR) 中。我们开发了一种数据驱动、无监督的方法,从护理记录中发现 RR 事件的潜在危险因素。方法我们应用多种自然语言处理方法,包括语言建模、词嵌入和两种短语挖掘方法(TextRank 和 NC-Value),从未注释的护理笔记中识别代表临床实体的高质量短语。 TextRank 用于确定每个注释中的重要单词序列。然后使用 NC-Value 对整个语料库中局部重要的序列进行全局排名。我们评估了我们的方法与人类判断相比的准确性,以及挖掘的短语预测临床结果、RR 事件风险的能力。结果当应用于 61,740 例医院就诊、1,067 个 RR 事件和 778,955 个注释时,我们的方法实现了 0.590 至 0.764 的平均精度(排除数字标记时)。使用短语的时间相关协变量 Cox 模型达到了 0.739 的一致性指数。对短语进行聚类揭示了与 RR 事件危险显着相关的临床概念。讨论我们的研究结果表明,我们的最少注释、毫不奇怪的方法可以从大量护理笔记中快速挖掘高质量短语,并且这些识别出的短语对于下游任务非常有用,例如临床结果预测和风险因素识别。
更新日期:2020-01-04
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