Weibing Long, Kunli Zhang, Hongchao Ma, Donghui Yue, Zhuang Lei
{"title":"基于神经网络多标签学习的产科辅助诊断双标签识别增强方法","authors":"Weibing Long, Kunli Zhang, Hongchao Ma, Donghui Yue, Zhuang Lei","doi":"10.1109/CYBERC.2018.00060","DOIUrl":null,"url":null,"abstract":"The data-driven medical health information processing has become a new development direction, especially the auxiliary diagnosis based on the electronic medical records (EMRs), which is of great significance to improve population health. In this paper, to obtain excellent obstetric auxiliary diagnostic results, the Chinese obstetric EMRs is analyzed and processed, and finally the auxiliary diagnosis task is transformed into a multi-label classification problem. Moreover, two effective global error functions are proposed by enhancing pairwise labels discrimination to improve the Backpropagation for Multi-label Learning (BP-MLL) that depends on the neural network model. The experiment results of some public multi-label datasets and the Chinese obstetric dataset show that the two error functions have better overall performance compared with BP-MLL original error function and some well-established multi-label learning algorithms.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural Network Multi-label Learning Based on Enhancing Pairwise Labels Discrimination for Obstetric Auxiliary Diagnosis\",\"authors\":\"Weibing Long, Kunli Zhang, Hongchao Ma, Donghui Yue, Zhuang Lei\",\"doi\":\"10.1109/CYBERC.2018.00060\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The data-driven medical health information processing has become a new development direction, especially the auxiliary diagnosis based on the electronic medical records (EMRs), which is of great significance to improve population health. In this paper, to obtain excellent obstetric auxiliary diagnostic results, the Chinese obstetric EMRs is analyzed and processed, and finally the auxiliary diagnosis task is transformed into a multi-label classification problem. Moreover, two effective global error functions are proposed by enhancing pairwise labels discrimination to improve the Backpropagation for Multi-label Learning (BP-MLL) that depends on the neural network model. The experiment results of some public multi-label datasets and the Chinese obstetric dataset show that the two error functions have better overall performance compared with BP-MLL original error function and some well-established multi-label learning algorithms.\",\"PeriodicalId\":282903,\"journal\":{\"name\":\"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CYBERC.2018.00060\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CYBERC.2018.00060","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural Network Multi-label Learning Based on Enhancing Pairwise Labels Discrimination for Obstetric Auxiliary Diagnosis
The data-driven medical health information processing has become a new development direction, especially the auxiliary diagnosis based on the electronic medical records (EMRs), which is of great significance to improve population health. In this paper, to obtain excellent obstetric auxiliary diagnostic results, the Chinese obstetric EMRs is analyzed and processed, and finally the auxiliary diagnosis task is transformed into a multi-label classification problem. Moreover, two effective global error functions are proposed by enhancing pairwise labels discrimination to improve the Backpropagation for Multi-label Learning (BP-MLL) that depends on the neural network model. The experiment results of some public multi-label datasets and the Chinese obstetric dataset show that the two error functions have better overall performance compared with BP-MLL original error function and some well-established multi-label learning algorithms.