Open Access Te Herenga Waka-Victoria University of Wellington
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Ontology-Guided Data Augmentation for Medical Document Classification

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conference contribution
posted on 2020-10-27, 23:35 authored by Mahdi Abdollahi, Gao Xiaoying, Mei Yi, Ghosh Shameek, Li Jinyan
Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.


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Abdollahi, M., Xiaoying, G., Yi, M., Shameek, G. & Jinyan, L. (2020, September). Ontology-Guided Data Augmentation for Medical Document Classification.

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