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Dataset Open Access

CodiEsp corpus training and development set: Spanish clinical cases coded in ICD10 (CIE10) - eHealth CLEF2020

Antonio Miranda; Aitor Gonzalez-Agirre; Martin Krallinger

These are the train and development sets of the CodiEsp corpus, released in the context of the CodiEsp track for CLEF ehealth 2020.

The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish version of ICD10-CM and ICD10-PCS). The CodiEsp corpus has been randomly sampled into three subsets: the train, the development, and the test set. The train set contains 500 clinical cases, and the development and test set 250 clinical cases each. The current version of the corpus does not contain the test set.

Corpus format description: The CodiEsp corpus is distributed in plain text in UTF8 encoding, where each clinical case is stored as a single file whose name is the clinical case identifier. Annotations are released in a tab-separated file. Since the CodiEsp track has 3 sub-tracks, every set of documents (train and test) has 3 tab-separated files associated with it. 

For the sub-tracks 1 and 2, the file has the following fields:

articleID label ICD10-code text-reference

Tab-separated files for the third sub-track contain an extra field that provides the position in the text of the text-reference:

articleID label ICD10-code text-reference reference-position

Corpus summary statistics: The final collection of 1000 clinical cases that make up the corpus had a total of 16504 sentences, with an average of 16.5 sentences per clinical case. It contains a total of 396,988 words, with an average of 396.2 words per clinical case.

For more information, visit the track webpage:

Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
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  • Villegas M, de la Peña S, Intxaurrondo A, Santamaria J, Krallinger M. Esfuerzos para fomentar la minería de textos en biomedicina más allá del inglés: el plan estratégico nacional español para las tecnologías del lenguaje. Procesamiento del Lenguaje Natural. 2017(59):141-4.

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