Avoiding Overfitting in Deep Neural Networks for Clinical Opinions Generation from General Blood Test Results

摘要：

　　We have used deep neural networks(DNNs)to generate clinical opinions from general blood test results.DNNs have overfitting problem in general.We believe the complex structure of DNN and insufficient data to be the major reasons of overfitting in our case.In this paper,we apply dropout and batch normalization to avoid overfitting.Experimental results show the improvement in the performance of the DNNs.

关键词： Hematologic Tests Neural Networks (Computer) Clinical Decision-Making

作者: Youjin Kim Han-Gyu Kim Zhun Li Ho-Jin Choi

作者单位: School of Computing,KAIST,Daejeon,Korea

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 1274-1274

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）