{'fraction_non_alphanumeric': 0.028368794326241134, 'fraction_numerical': 0.04609929078014184, 'mean_word_length': 4.76530612244898, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SITIS-ISPED in CLEF eHealth 2018 Task 1 : ICD10 coding using Deep Learning

This paper presents SITIS-ISPED’s participation in the Task 1 : Multilingual Information Extraction ICD10 coding of the CLEF eHealth 2018 challenge which focuses on extraction of causes of death from a corpus of death reports. We used OpenNMT, a deep learning framework specialized in sequence-to-sequence models. An encoder-decoder model with attention transformed diagnostics into ICD10 codes. Our system obtained F-scores of 0.4368 for the raw corpus and 0.5385 for the aligned corpus.