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KCL-Health-NLP@CLEF eHealth 2018 Task 1: ICD-10 coding of French and Italian death certificates with character-level convolutional neural networks

Research output: Contribution to journalConference paper

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2125
Publication statusPublished - 1 Jan 2018
Event19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018 - Avignon, France
Duration: 10 Sep 201814 Sep 2018

King's Authors

Abstract

In this paper we describe the participation of the KCL-Health-NLP team in the CLEF eHealth 2018 lab, specifically Task 1: Multilingual Information Extraction-ICD10 coding. The task involves the automatic coding of causes of death in death certificates in French, Italian and Hungarian according to the ICD-10 taxonomy. Choosing to work on the two Romance languages, we treated the task as a sequence-to-sequence prediction problem. Our system has an encoder-decoder architecture, with convolutional neural networks based on character em-beddings as encoders and recurrent neural network decoders. Our hypothesis was that a character-level representation would allow our model to generalise across two genealogically related languages. Results obtained by pre-training our Italian model on the French data set confirmed this intuition. We also explored the impact of character-level features extracted from dictionary-matched ICD codes. We obtained F-measures of 0.72/0.64 and 0.78 on the French aligned/raw and Italian raw internal test data, respectively. On the blind test set released by the task organisers, our top results were 0.65/0.52 and 0.69 F-measure, respectively.

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