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 in conjunction with support vector machines for ICD10 code classification. First, we composed a large scale feature set comprising more than 40k features based on bag of words, bag of 2-grams, bag of 3-grams, latent Dirichlet allocation, and the ontologies of WordNet and UMLS. 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 vote, maximum, and average. For the English test set, the best F-measure was 0.8187 using early fusion. For the two French test sets, we achieved 0.6692 and 0.7216 using late fusion in connection with the rule average for bag of words and bag of 2-grams.