Chang Edward K, Yu Christine Y, Clarke Robin, Hackbarth Andrew, Sanders Timothy, Esrailian Eric, Hommes Daniel W, Runyon Bruce A
Divisions of *Digestive Diseases †General Internal Medicine, David Geffen School of Medicine at UCLA ‡UCLA Office of Health Informatics & Analytics, UCLA Health, Los Angeles, CA.
J Clin Gastroenterol. 2016 Nov/Dec;50(10):889-894. doi: 10.1097/MCG.0000000000000583.
The objective of this study was to use natural language processing (NLP) as a supplement to International Classification of Diseases, Ninth Revision (ICD-9) and laboratory values in an automated algorithm to better define and risk-stratify patients with cirrhosis.
Identification of patients with cirrhosis by manual data collection is time-intensive and laborious, whereas using ICD-9 codes can be inaccurate. NLP, a novel computerized approach to analyzing electronic free text, has been used to automatically identify patient cohorts with gastrointestinal pathologies such as inflammatory bowel disease. This methodology has not yet been used in cirrhosis.
This retrospective cohort study was conducted at the University of California, Los Angeles Health, an academic medical center. A total of 5343 University of California, Los Angeles primary care patients with ICD-9 codes for chronic liver disease were identified during March 2013 to January 2015. An algorithm incorporating NLP of radiology reports, ICD-9 codes, and laboratory data determined whether these patients had cirrhosis. Of the 5343 patients, 168 patient charts were manually reviewed at random as a gold standard comparison. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of the algorithm and each of its steps were calculated.
The algorithm's PPV, NPV, sensitivity, and specificity were 91.78%, 96.84%, 95.71%, and 93.88%, respectively. The NLP portion was the most important component of the algorithm with PPV, NPV, sensitivity, and specificity of 98.44%, 93.27%, 90.00%, and 98.98%, respectively.
NLP is a powerful tool that can be combined with administrative and laboratory data to identify patients with cirrhosis within a population.
本研究的目的是在一种自动化算法中使用自然语言处理（NLP）作为对国际疾病分类第九版（ICD - 9）和实验室检查值的补充，以更好地定义肝硬化患者并对其进行风险分层。
通过人工数据收集来识别肝硬化患者既耗时又费力，而使用ICD - 9编码可能不准确。NLP是一种用于分析电子自由文本的新型计算机化方法，已被用于自动识别患有胃肠道疾病（如炎症性肠病）的患者群体。这种方法尚未应用于肝硬化的研究。
这项回顾性队列研究在学术医疗中心加利福尼亚大学洛杉矶分校健康中心进行。在2013年3月至2015年1月期间，共识别出5343名加利福尼亚大学洛杉矶分校初级保健患者，他们具有慢性肝病的ICD - 9编码。一种结合了放射学报告的NLP、ICD - 9编码和实验室数据的算法，用于确定这些患者是否患有肝硬化。在这5343名患者中，随机抽取168份患者病历作为金标准对照进行人工审核。计算了该算法及其每个步骤的阳性预测值（PPV）、阴性预测值（NPV）、敏感性和特异性。
该算法的PPV、NPV、敏感性和特异性分别为91.78%、96.84%、95.71%和93.88%。NLP部分是该算法最重要的组成部分，其PPV、NPV、敏感性和特异性分别为98.44%、93.27%、90.00%和98.98%。
NLP是一种强大的工具，可以与管理数据和实验室数据相结合，以识别特定人群中的肝硬化患者。
