CodiEsp-abstracs: Abstracts from Lilacs and Ibecs with ICD10 codes

JSON file with abstracts from Lilacs and Ibecs with ICD10 codes (ICD10-CM and ICD10-PCS) associated to them (CIE10 in Spanish). <strong>Please, cite us:</strong> Miranda-Escalada, A., Gonzalez-Agirre, A., Armengol-Estapé, J., Krallinger, M.: Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of eHealth CLEF 2020.<em> In: CLEF (Working Notes) (2020)</em> <pre><code>@inproceedings{miranda2020overview, </code> <code>title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, </code> <code>author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, </code> <code>booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, </code> <code>year={2020} }</code></pre> Lilacs and Ibecs databases have MeSH terms describing some of their documents. Then, using UMLS Metathesaurus, those MeSH terms have been translated into ICD10 codes (ICD10-CM and ICD10-PCS). Every abstract have at least one ICD10 code. In addition, MeSH codes given by the databases (Lilacs and Ibecs) have a "word" describing them. These "words" have been used to add further ICD10 codes. We have done strict string matching to find whether those "words" were a descriptor of any ICD10 code (in the Spanish version, CIE10). The format of the JSON file is the following: <pre>{'articles': [{'title': 'title', 'pmid': 'pmid', 'abstractText': 'abtract (in Spanish)', 'Mesh': [{'Code': 'MeSHCode', 'Word': 'reference', 'CIE': [CIE10_1, CIE10_2, ...]}, ...] }, ...] }</pre> Additionally, the compressed file includes a folder with all the abstracts extracted in individual UTF-8 encoded text files and a tab-separated file with 4 fields: pmid label cie10-code word Summary statistics: number of abstracts: 355 840 number abstracts with at least one ICD10 code: 176 294 Percentage of MeSH codes mapped to ICD10: 10.6% (there were 2 526 772 MeSH codes and 266 949 mapped to ICD10) average number of MeSH codes per article: 7.1 average number of ICD10 codes per article: 2.5 number of ICD10 codes that have an associated MeSH code in UMLS: 3293 number of ICD10 codes that have an associated MeSH code in UMLS and appear in this dataset: 3082本数据集为存储Lilacs与Ibecs数据库文摘的JSON文件，所有文摘均已关联ICD-10编码（含ICD10-CM与ICD10-PCS，西班牙语版本称CIE10）。<strong>敬请引用：</strong> Miranda-Escalada, A., Gonzalez-Agirre, A., Armengol-Estapé, J., Krallinger, M.: Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of eHealth CLEF 2020.<em> In: CLEF (Working Notes) (2020)</em> <pre><code>@inproceedings{miranda2020overview, </code> <code>title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, </code> <code>author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estapé, Jordi and Krallinger, Martin}, </code> <code>booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, </code> <code>year={2020} }</code></pre> Lilacs与Ibecs数据库的部分文档已标注医学主题词表（MeSH）术语。随后借助统一医学语言系统（UMLS）元词典（Metathesaurus），将这些MeSH术语映射为ICD-10编码（ICD10-CM与ICD10-PCS）。每篇文摘至少对应一个ICD-10编码。此外，数据库（Lilacs与Ibecs）提供的MeSH编码均带有描述性“词汇”，我们采用严格字符串匹配的方法，验证这些“词汇”是否为西班牙语版CIE10编码的描述符，从而补充额外的ICD-10编码。 该JSON文件的格式如下： <pre>{'articles': [{'title': 'title', 'pmid': 'pmid', 'abstractText': 'abtract (in Spanish)', 'Mesh': [{'Code': 'MeSHCode', 'Word': 'reference', 'CIE': [CIE10_1, CIE10_2, ...]}, ...] }, ...] }</pre> 此外，压缩包包含一个文件夹，内含所有以UTF-8编码的独立文本文件形式存储的文摘，以及一个制表符分隔的文件，该文件包含4个字段：pmid、标签、CIE10编码、词汇。 数据集统计信息如下： - 文摘总数：355,840篇 - 至少包含一个ICD-10编码的文摘数：176,294篇 - MeSH编码映射至ICD-10的占比：10.6%（共计2,526,772个MeSH编码，其中266,949个成功映射至ICD-10） - 单篇文摘平均MeSH编码数：7.1 - 单篇文摘平均ICD-10编码数：2.5 - 通过UMLS关联MeSH编码的ICD-10编码总数：3,293个 - 本数据集中出现的、通过UMLS关联MeSH编码的ICD-10编码数：3,082个