CLEF eHealth 2017 Multilingual Information Extraction task Overview: ICD10 Coding of Death Certificates in English and French

نویسندگان

  • Aurélie Névéol
  • Aude Robert
  • Robert Anderson
  • K. Bretonnel Cohen
  • Cyril Grouin
  • Thomas Lavergne
  • Grégoire Rey
  • Claire Rondet
  • Pierre Zweigenbaum
چکیده

This paper reports on Task 1 of the 2017 CLEF eHealth evaluation lab which extended the previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task continued with coding of death certificates, as introduced in CLEF eHealth 2016. This largescale classification task consisted of extracting causes of death as coded in the International Classification of Diseases, tenth revision (ICD10). The languages offered for the task this year were English and French. Participant systems were evaluated against a blind reference standard of 31,690 death certificates in the French dataset and 6,665 certificates in the English dataset using Precision, Recall and F-measure. In total, eleven teams participated: 10 teams submitted runs for the English dataset and 9 for the French dataset. Five teams submitted their systems to the reproducibility track. For death certificate coding, the highest performance was 0.8674 F-measure for French and 0.8501 for English.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

SIBM at CLEF eHealth Evaluation Lab 2017: Multilingual Information Extraction with CIM-IND

This paper presents SIBM’s participation in the Task 1: Multilingual Information Extraction ICD10 coding of the CLEF eHealth 2017 evaluation initiative which focuses on named entity recognition in French and English death certificates. We addressed the identification of relevant clinical entities within the International Classification of Diseases version 10 (ICD10) in the CépiDC and CDC datase...

متن کامل

A Reproducible Approach with R Markdown to Automatic Classification of Medical Certificates in French

English. In this paper, we report the ongoing developments of our first participation to the Cross-Language Evaluation Forum (CLEF) eHealth Task 1: “Multilingual Information Extraction ICD10 coding” (Névéol et al., 2017). The task consists in labelling death certificates, in French with international standard codes. In particular, we wanted to accomplish the goal of the ‘Replication track’ of t...

متن کامل

Fusion Methods for ICD10 Code Classification of Death Certificates in Multilingual Corpora

In this working notes paper, we present our methodology and the results for Task 1 of the CLEF eHealth Evaluation Lab 2017. This benchmark addresses information extraction in written text with focus on unexplored languages corpora, specifically English and French. The goal is to automatically assign codes (ICD10) to text content of death certificates. Our approach is focused on fusion methods i...

متن کامل

KFU at CLEF eHealth 2017 Task 1: ICD-10 Coding of English Death Certificates with Recurrent Neural Networks

This paper describes the participation of the KFU team in the CLEF eHealth 2017 challenge. Specifically, we participated in Task 1, namely “Multilingual Information Extraction ICD-10 coding” for which we implemented recurrent neural networks to automatically assign ICD10 codes to fragments of death certificates written in English. Our system uses Long Short-Term Memory (LSTM) to map the input s...

متن کامل

Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016

This paper reports on Task 2 of the 2016 CLEF eHealth evaluation lab which extended the previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task continued with named entity recognition and normalization in French narratives, as offered in CLEF eHealth 2015. Named entity recognition involved ten types of entities including disorders that were defined according to Sem...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017