Challenges in clinical natural language processing for automated disorder normalization.
Challenges in clinical natural language processing for automated disorder normalization.
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DOI：
10.1016/j.jbi.2015.07.010
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发表时间：
2015-10
影响因子：
4.5
通讯作者：
Lu Z
中科院分区：
文献类型：
--
作者：
Leaman R;Khare R;Lu Z
识别电子健康记录中临床叙述中的疾病等关键变量在临床实践和生物医学研究中具有广泛的应用。先前的研究表明，与生物医学出版物相比，临床叙述中的命名实体识别（NER）和标准化（或基础）障碍的表现有所下降。在这项工作中，我们的目标是找出造成这种性能差异的原因并引入通用解决方案。我们使用封闭性来比较临床叙述文本和生物医学出版物中词汇的丰富程度。我们使用机器学习方法来处理无序 NER 和标准化。我们的 NER 方法基于具有丰富特征方法的线性链条件随机场，并且我们引入了一些改进来增强 NER 系统的词汇知识。我们的归一化方法（以前从未应用于临床数据）使用成对学习进行排名，以直接从训练数据中自动学习术语变异。我们发现，虽然临床叙述和生物医学出版物的总体词汇量相似，但临床叙述使用比出版物更丰富的术语来描述疾病。我们应用我们的系统 DNorm-C 来定位最近的 ShARe/CLEF 电子健康任务中的疾病提及和临床叙述。对于 NER（仅限严格跨度），我们的系统实现精度 = 0.797，召回率 = 0.713，f 分数 = 0.753。对于标准化任务（严格跨度 + 概念），它实现了精度 = 0.712，召回率 = 0.637，f 分数 = 0.672。本文中描述的改进将 NER f 分数提高了 0.039，将归一化 f 分数提高了 0.036。我们还描述了 NER 的高召回率版本，它将归一化召回率提高到高达 0.744，尽管精度有所降低。我们进行了错误分析，证明 NER 错误与归一化错误的数量之比超过 4 比 1。除了注释者无法在受控词汇范围内识别的提及之外，缩写词和首字母缩略词被发现是常见的错误原因。临床叙述文本中提到的疾病使用了丰富的词汇，导致术语变化较大，我们认为这是临床叙述表现下降的主要原因之一。我们表明，成对学习排序在这种情况下提供了高性能，并引入了一些词汇增强功能（可推广到其他临床 NER 任务），从而提高 NER 系统处理这种变化的能力。 DNorm-C 是一个针对临床文本中的疾病的高性能开源系统，也是朝着可针对各种领域和实体进行训练的 NER 和标准化方法迈出的有希望的一步。 DNorm-C 是开源软件，可在 DNorm 演示网站上使用经过训练的模型：http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#DNorm。
Identifying key variables such as disorders within the clinical narratives in electronic health records has wide-ranging applications within clinical practice and biomedical research. Previous research has demonstrated reduced performance of disorder named entity recognition (NER) and normalization (or grounding) in clinical narratives than in biomedical publications. In this work, we aim to identify the cause for this performance difference and introduce general solutions. We use closure properties to compare the richness of the vocabulary in clinical narrative text to biomedical publications. We approach both disorder NER and normalization using machine learning methodologies. Our NER methodology is based on linear-chain conditional random fields with a rich feature approach, and we introduce several improvements to enhance the lexical knowledge of the NER system. Our normalization method – never previously applied to clinical data - uses pairwise learning to rank to automatically learn term variation directly from the training data. We find that while the size of the overall vocabulary is similar between clinical narrative and biomedical publications, clinical narrative uses a richer terminology to describe disorders than publications. We apply our system, DNorm-C, to locate disorder mentions and in the clinical narratives from the recent ShARe/CLEF eHealth Task. For NER (strict span-only), our system achieves precision = 0.797, recall = 0.713, f-score = 0.753. For the normalization task (strict span + concept) it achieves precision = 0.712, recall = 0.637, f-score = 0.672. The improvements described in this article increase the NER f-score by 0.039 and the normalization f-score by 0.036. We also describe a high recall version of the NER, which increases the normalization recall to as high as 0.744, albeit with reduced precision. We perform an error analysis, demonstrating that NER errors outnumber normalization errors by more than 4-to-1. Abbreviations and acronyms are found to be frequent causes of error, in addition to the mentions the annotators were not able to identify within the scope of the controlled vocabulary. Disorder mentions in text from clinical narratives use a rich vocabulary that results in high term variation, which we believe to be one of the primary causes of reduced performance in clinical narrative. We show that pairwise learning to rank offers high performance in this context, and introduce several lexical enhancements – generalizable to other clinical NER tasks – that improve the ability of the NER system to handle this variation. DNorm-C is a high performing, open source system for disorders in clinical text, and a promising step towards NER and normalization methods that are trainable to a wide variety of domains and entities. DNorm-C is open source software, and is available with a trained model at the DNorm demonstration website: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#DNorm.