Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction
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RESEARCH
Open Access
Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction Zhichang Zhang1*† , Yanlong Qiu2† , Xiaoli Yang3 and Minyu Zhang4 From 5th China Health Information Processing Conference Guangzhou, China. 22-24 November 2019
Abstract Background: Electronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disease (CVD), and are able to use them to predict CVD, we have the ability to automatically process clinical texts, resulting in an improved accuracy of supporting doctors for the clinical diagnosis of CVD. In the case where CVD is becoming more worldwide, predictive CVD based on EMRs has been studied by many researchers to address this important aspect of improving diagnostic efficiency. Methods: This paper proposes an Enhanced Character-level Deep Convolutional Neural Networks (EnDCNN) model for cardiovascular disease prediction. Results: On the manually annotated Chinese EMRs corpus, our risk factor identification extraction model achieved 0.9073 of F-score, our prediction model achieved 0.9516 of F-score, and the prediction result is better than the most previous methods. Conclusions: The character-level model based on text region embedding can well map risk factors and their labels as a unit into a vector, and downsampling plays a crucial role in improving the training efficiency of deep CNN. What’s more, the shortcut connections with pre-activation used in our model architecture implements dimension-matching free in training. Keywords: Chinese electronic medical record, CVD risk factors extraction, CVD prediction, Downsampling, Pre-activation, Dimension-matching free, Text region embedding
*Correspondence: [email protected] † Zhichang Zhang and Yanlong Qiu contributed equally to this work and are co-first authors 1 College of Computer Science and Engineering, Northwest Normal University, 967 Anning East Road, 730070, Lanzhou, China Full list of author information is available at the end of the article © The Author(s). 2020 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (h
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