Toronto CL CLEF 2018 eHealth Task 1: Multi-lingual ICD-10 Coding using an Ensemble of Recurrent and Convolutional Neural Networks

Clef ehealth 2018 multilingual information extraction task overview: Icd10 coding of death certificates in french, hungarian and italian. In CLEF 2018 Evaluation Labs and

Similarly, pre-training the Italian model with weights derived from the French models using string matching codes did not support the neural network training. This represents

Bretonnel Cohen, Cyril Grouin, Thomas Lavergne, Gregoire Rey, Aude Robert, Claire Rondet, and Pierre Zweigenbaum, CLEF eHealth 2017 Multilingual Information Extraction task

Here, the original ICD-10 code mappings, found in the supplied dictionaries, are extended with the training instances from individual certificate data from the three

Team SIBM obtained the best official performance in terms of F-measure both overall and for the external causes of death on the French datasets.. It is interesting to note that

Our results demonstrate that the performance of dictionary lookup based approach for ICD-10 code assignment in death certificates is inferior to supervised and/or hybrid

Keywords: ICD-10 coding, ICD-10 codes, medical concept coding, re- current neural network, sequence to sequence, sequence-to-sequence ar- chitecture, encoder-decoder model,

Finally, the top candidate, generated from the overlap between the list of possible ICD-10 code candidates (input list) and ranked list of final ICD-10 candidates (output list),