MATEC Web Conf.
Volume 189, 20182018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
|Number of page(s)||6|
|Section||Bio Issues & Environment|
|Published online||10 August 2018|
Automatic summarization of medical interviews
Department of computer science, Yangzhou University, Yangzhou, Jiangsu, China
Corresponding author: email@example.com
Abstract. The genomic-based targeted therapy (Crizotinib) has been emerged as an alternative option for the treatment of patients with locally advanced or metastatic non-small cell lung cancer, comprising the 85\% of lung cancer. However, Crizotinib is not listed in VA drug formulary- and is not available for VA oncologists to treat lung cancer currently. Therefore, for understanding physicians’ views on using genomic services, semi-structured interviews were collected. In this paper, we will present an innovative method to extract summarization from medical interviews automatically. Different from keyword-based method, automatic summarization can help to understand the intention of physicians. Compared with the existing summarization methods, our work is based on latent Dirichlet allocation and recent results m word embeddings that learn seinantically meaningful representations for words from local cooccurrences in sentences. Experiments on medical interviews demonstrate that the proposed algorithm achieves good results compared with a gold standard file using manual extraction technique.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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