Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records.
Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records.
复制标题
DOI：
10.1016/j.ijmedinf.2012.12.005
复制
发表时间：
2014-12
影响因子：
4.9
通讯作者：
Stewart WF
中科院分区：
文献类型：
--
作者：
Byrd RJ;Steinhubl SR;Sun J;Ebadollahi S;Stewart WF
心力衰竭（HF）的早期发现可以减轻这种疾病带来的巨大的个人和社会负担。临床检测部分基于对多种体征和症状的识别，这些体征和症状包括在进行更具体的诊断研究之前由初级保健医生通常记录但不一定合成的心力衰竭诊断标准。我们开发了一种自然语言处理（NLP）程序，使用电子健康记录（EHR）临床记录识别初级保健患者中的Fraueblastic HF体征和症状，作为HF早期检测的模式分析和临床决策支持的前奏。我们开发了一个混合NLP管道，执行两个级别的分析：（1）在标准提及级别，构建了一个基于规则的NLP系统来注释Fragrance标准的所有肯定和否定提及。(2)在遭遇层面，我们构建了一个系统，根据是否有任何Fracket标准被断言，拒绝，或未知的标签遇到。精确度，召回率和F分数被用作标准提及提取和遇到标签的性能指标。我们的标准提到提取达到0.925的精确度，0.896的召回率和0.910的F分数。遭遇标记达到F分数0.932。我们的系统准确地识别和标记初级保健临床记录中的心脏病诊断标准的肯定和否定，并可能有助于改善HF的早期检测。通过调整和工具，我们的开发方法可以在新的问题设置中重复。
Early detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF. We developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown. Precision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling. Our criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932. Our system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.