Extraction of left ventricular ejection fraction information from various types of clinical reports
Extraction of left ventricular ejection fraction information from various types of clinical reports
复制标题
DOI：
10.1016/j.jbi.2017.01.017
复制
发表时间：
2017-03-01
影响因子：
4.5
通讯作者：
Meystre, Stephane M.
中科院分区：
文献类型：
--
作者：
Kim, Youngjun;Garvin, Jennifer. H.;Meystre, Stephane M.
努力改善充血性心力衰竭的治疗，一种常见的和严重的医疗条件，包括使用质量措施，以评估指南一致的护理。本研究的目的是从各种类型的临床记录中识别左心室射血分数（LVEF）信息，然后将此信息用于心力衰竭质量测量。我们分析了来自超声心动图，放射学和文本集成实用程序包的新临床笔记语料库与退伍军人事务部自然语言处理（NLP）研究注释的其他语料库之间的注释差异。这些报告包含不同程度的结构。为了检查我们在先前研究中开发的现有LVEF提取模块是否提高了从新语料库中提取LVEF信息的准确性，我们创建了两个序列标记NLP模块，这些模块使用新数据集进行训练，有或没有来自现有LVEF提取模块的预测。我们还进行了一组实验来研究训练数据大小对信息提取准确性的影响。我们发现，当报告高度结构化时，需要较少的训练数据，并且当报告具有较少的结构化格式和丰富的词汇集时，结合现有LVEF提取模块的预测可以提高信息提取。(C)2017由Elsevier Inc.出版
Efforts to improve the treatment of congestive heart failure, a common and serious medical condition, include the use of quality measures to assess guideline-concordant care. The goal of this study is to identify left ventricular ejection fraction (LVEF) information from various types of clinical notes, and to then use this information for heart failure quality measurement. We analyzed the annotation differences between a new corpus of clinical notes from the Echocardiography, Radiology, and Text Integrated Utility package and other corpora annotated for natural language processing (NLP) research in the Department of Veterans Affairs. These reports contain varying degrees of structure. To examine whether existing LVEF extraction modules we developed in prior research improve the accuracy of LVEF information extraction from the new corpus, we created two sequence-tagging NLP modules trained with a new data set, with or without predictions from the existing LVEF extraction modules. We also conducted a set of experiments to examine the impact of training data size on information extraction accuracy. We found that less training data is needed when reports are highly structured, and that combining predictions from existing LVEF extraction modules improves information extraction when reports have less structured formats and a rich set of vocabulary. (C) 2017 Published by Elsevier Inc.