ECSTRA-APHP @ CLEF eHealth2018-task 1: ICD10 Code Extraction from Death Certificates

KCL-Health- NLP@CLEF eHealth 2018 Task 1: ICD-10 Coding of French and Italian Death Certificates with Character-Level Convolutional Neural Networks CLEF 2018 On- line Working Notes.

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

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

In particular, we were interested in automatically gathering medical acronyms from a Wikipedia page and manually cleaning the table of expanded acronyms (for example,

In the development phase, we evaluated three different methods: each feature type separately (no fusion), early feature-level fusion, and late fusion including the rules majority

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,