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	Automatic Text Classification for Label Imputation of Medical Diagnosis Notes based on Random Forest
	Yujie Yang; Bokai Yang; Guangzhe Dai; Darong Tang; Qi Li; Denan Lin; Jing Zheng; Yunpeng Cai
	2018
会议日期	2018
会议地点	Cairns, Queensland, Australia
英文摘要	Electronic medical records (EMRs) contain many information of pa- tients, which are of great value for data mining for various clinical applications. However, information missing, including label missing, is pervasive in nature EMRs which would bring lots of obstacles for processing of the medical text contents. The aim of this study is to adopt automatic text classification technolo- gies to recover missing medical text labels for EMRs and support downstream analyses. A combination of word-embedding technology and random forest clas- sifiers are applied to identify multiple medical note labels including disease types and examination types, from short texts of medical imaging diagnosis notes. The results show that the average binary classification accuracies are 91%. Our re- search results indicate that using advanced NLP techniques for EMRs can reach high classification accuracies.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/14151]
专题	深圳先进技术研究院_数字所
推荐引用方式 GB/T 7714	Yujie Yang,Bokai Yang,Guangzhe Dai,et al. Automatic Text Classification for Label Imputation of Medical Diagnosis Notes based on Random Forest[C]. 见:. Cairns, Queensland, Australia. 2018.

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