Deep Learning Approach for Semantic Indexing of Animal Experiments Summaries in German Language

Semantic indexing of animal experiment summaries is the process of annotating the summaries with its medical codes. Semantic indexing is helpful in reducing time and performance in knowing the context and finding relevant summaries. Indexing the Non-Technical Summaries (NTP)s using codes from the German version of the International Classification of Diseases (ICD-10) is a challenging task. ICD-10 codes, which is a comprehensive way of storing the health conditions are useful for the identification of many disorders, diseases and other health related problems. Thus, annotating the NTPs with codes will make the way of storing, organising, retrieval and comparing the health information more easier. In our paper, we have approached the problem using deep neural network. This work is evaluated on the dataset given by eHealth@CLEF2019. The test set given by the task is used to evaluate our methodology which attains precision, recall and f1 score of 0.19, 0.27 and 0.23 for Run 1 , 0.19, 0.27 and 0.22 for Run 2 and 0.13, 0.34 and 0.19 for Run 3 respectively. The performance of our method can further be increased by considering other recurrent units.

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