计算机应用与软件2017，Vol.34Issue(12)：58-63,6.DOI:10.3969/j.issn.1000-386x.2017.12.011
基于序列标注算法比较的医学文献风险事件抽取研究
RESEARCH ON MEDICAL DOCUMENT RISK EVENT EXTRACTION BASED ON COMPARISON OF SEQUENCE MARKING ALGORITHMS
摘要
Abstract
With the rapid growth of medical literature,it is a huge challenge to extract valuable knowledge from big data in medical literature text.This paper focused on the event extraction of quantitative risk statements in medical literature,and constructed the knowledge base of intelligent clinical decision support system.Firstly,the risk events corresponding to the quantitative risk information were extracted from the medical literature,and then the risk events were processed.The hidden Markov model,the maximum entropy Markov model and the conditional random field model were used to extract the information of the risk events in medical literature unstructured full text,and the algorithms were compared.From the average F1 of three models,conditional random field was the best,followed by maximum entropy Markov model,and then the hidden Markov model,but each model had its own advantage of certain event extraction accuracy or recall.关键词
医学文献/风险事件/隐马尔可夫模型/最大熵马尔可夫模型/条件随机场Key words
Medical literature/Risk event/Hidden Markov model/Maximum entropy Markov model/Conditional random field分类
信息技术与安全科学引用本文复制引用
喻鑫,张矩,邱武松,王飞..基于序列标注算法比较的医学文献风险事件抽取研究[J].计算机应用与软件,2017,34(12):58-63,6.基金项目
重庆市社会民生科技创新专项项目(cstc2015shmszx120025). （cstc2015shmszx120025）
