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Journal of Healthcare Engineering
Volume 2018 (2018), Article ID 7273451, 9 pages
https://doi.org/10.1155/2018/7273451
Research Article

The Comparative Experimental Study of Multilabel Classification for Diagnosis Assistant Based on Chinese Obstetric EMRs

1Information Engineering School, Zhengzhou University, Zhengzhou, Henan 450000, China
2Industrial Technology Research, Zhengzhou University, Zhengzhou, Henan 450000, China
3The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China

Correspondence should be addressed to Hongchao Ma; moc.liamxof@ch-am

Received 25 August 2017; Revised 3 December 2017; Accepted 14 December 2017; Published 5 February 2018

Academic Editor: Maria Lindén

Copyright © 2018 Kunli Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Obstetric electronic medical records (EMRs) contain massive amounts of medical data and health information. The information extraction and diagnosis assistants of obstetric EMRs are of great significance in improving the fertility level of the population. The admitting diagnosis in the first course record of the EMR is reasoned from various sources, such as chief complaints, auxiliary examinations, and physical examinations. This paper treats the diagnosis assistant as a multilabel classification task based on the analyses of obstetric EMRs. The latent Dirichlet allocation (LDA) topic and the word vector are used as features and the four multilabel classification methods, BP-MLL (backpropagation multilabel learning), RAkEL (RAndom k labELsets), MLkNN (multilabel k-nearest neighbor), and CC (chain classifier), are utilized to build the diagnosis assistant models. Experimental results conducted on real cases show that the BP-MLL achieves the best performance with an average precision up to 0.7413 ± 0.0100 when the number of label sets and the word dimensions are 71 and 100, respectively. The result of the diagnosis assistant can be introduced as a supplementary learning method for medical students. Additionally, the method can be used not only for obstetric EMRs but also for other medical records.