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. 2018 Apr 20;8:6329. doi: 10.1038/s41598-018-24389-w

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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The overall framework of the proposed model. We use the convolutional neural network to extract the semantic feature vectors of unstructured electronic medical records and map them to the feature space, finally we use the classifier to calculate the probable probability of each disease and select the highest probability of the disease as the auxiliary diagnosis of our model.