Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2.
Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2.
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DOI：
10.1186/s13326-016-0084-y
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发表时间：
2016-07-01
影响因子：
1.9
通讯作者：
Chapman WW
中科院分区：
文献类型：
--
作者：
Mowery DL;South BR;Christensen L;Leng J;Peltonen LM;Salanterä S;Suominen H;Martinez D;Velupillai S;Elhadad N;Savova G;Pradhan S;Chapman WW
ShaRe/CLEF eHealth 挑战实验室旨在促进自然语言处理和信息检索技术的发展，以帮助患者理解他们的临床报告。在临床文本中，首字母缩略词和缩写词（也称为缩写形式）可能很难让患者理解。对于 2013 年三项共享任务之一（任务 2），我们生成了标准化为统一医学语言系统的临床简表参考标准。该参考标准可用于通过链接到具有带注释的简短表格的通俗描述的网络资源或通过用更简化的通俗术语替换简短表格来提高患者的理解。在这项研究中，我们评估了 1) 与多数意义基线方法相比，参与系统的标准化简短形式的准确性，2) 参与者系统在具有可变多数意义分布的简短形式方面的性能，以及 3) 报告参与系统在测试集和消费者健康词汇（非专业医学术语词汇表）之间标准化共享标准化概念的准确性。五个参赛团队提交的最佳系统的准确率在 43% 到 72% 之间。多数感知基线方法取得了第二好的性能。参与系统对具有两种或多种低歧义意义（多数意义大于 80%）的简短形式进行规范化的性能范围为 52% 至 78%，对两种或多种具有中等歧义的意义（多数意义在 50% 至 80% 之间）的简短形式进行归一化的准确度范围为 23% 至 57%，对两种或多种高歧义意义（多数意义小于 50%）的简短形式进行归一化。准确度范围为 2% 到 45%。对于 ShaRe 测试集，69% 的简短注释包含消费者健康词汇表中的共同概念唯一标识符。对于这 2594 个可能的注释，参与系统的性能准确度在 50% 到 75% 之间。简短形式标准化仍然是一个具有挑战性的问题。简短的标准化系统具有中等至合理的精度。 Consumer Health Vocabulary 可以利用 ShaRe 测试集中遗漏的概念唯一标识符来丰富其知识库，以进一步支持患者对不熟悉的医学术语的理解。
The ShARe/CLEF eHealth challenge lab aims to stimulate development of natural language processing and information retrieval technologies to aid patients in understanding their clinical reports. In clinical text, acronyms and abbreviations, also referenced as short forms, can be difficult for patients to understand. For one of three shared tasks in 2013 (Task 2), we generated a reference standard of clinical short forms normalized to the Unified Medical Language System. This reference standard can be used to improve patient understanding by linking to web sources with lay descriptions of annotated short forms or by substituting short forms with a more simplified, lay term. In this study, we evaluate 1) accuracy of participating systems’ normalizing short forms compared to a majority sense baseline approach, 2) performance of participants’ systems for short forms with variable majority sense distributions, and 3) report the accuracy of participating systems’ normalizing shared normalized concepts between the test set and the Consumer Health Vocabulary, a vocabulary of lay medical terms. The best systems submitted by the five participating teams performed with accuracies ranging from 43 to 72 %. A majority sense baseline approach achieved the second best performance. The performance of participating systems for normalizing short forms with two or more senses with low ambiguity (majority sense greater than 80 %) ranged from 52 to 78 % accuracy, with two or more senses with moderate ambiguity (majority sense between 50 and 80 %) ranged from 23 to 57 % accuracy, and with two or more senses with high ambiguity (majority sense less than 50 %) ranged from 2 to 45 % accuracy. With respect to the ShARe test set, 69 % of short form annotations contained common concept unique identifiers with the Consumer Health Vocabulary. For these 2594 possible annotations, the performance of participating systems ranged from 50 to 75 % accuracy. Short form normalization continues to be a challenging problem. Short form normalization systems perform with moderate to reasonable accuracies. The Consumer Health Vocabulary could enrich its knowledge base with missed concept unique identifiers from the ShARe test set to further support patient understanding of unfamiliar medical terms.