摘要
本文主要研究深度学习在抗菌药物使用方法分类及数据挖掘应用,在现有的疾病和电子病历抗菌药物使用方法的文本数据挖掘过程中,利用基于注意力机制的长短期记忆网络模型训练抗菌药物语料数据,通过自我学习特征的方式表示和理解问题,避免人工特征的提取误差,使分类的准确率最大值较传统数据挖掘方法提高至89.97%,从而更好地为不同疾病患者提供相应的抗菌药物治疗方案.根据实验结果,该方法在不需要人工制定特征规则的条件下,可以自主学习生成治疗方案知识库,从而为医生治疗患者提供最佳的辅助决策支持.
In this study, we mainly focus on the application of deep learning in the classification of antimicrobial drug using methods and data mining. In the process of text data mining using existing methods of using antimicrobial drugs in disease and electronic medical records, we use the Long Short-Term Memory model (LSTM) based on attention model to train the data of antimicrobial drugs corpus, and express and understand the problems by means of self-learning features, so as to avoid the error of extracting artificial features. The maximum classification accuracy is increased to 89.97% compared with the traditional data mining method. As a result, it provides better antimicrobial treatment plans for patients with different diseases. According to the experimental results, the proposed method can automatically learn and generate treatment knowledge base without the need for manual rules, so as to provide the decision-making support for doctors to treat patients.
作者
梁治钢
王一敏
LIANG Zhi-Gang;WANG Yi-Min(Network Center, Gansu Provincial Hospital, Lanzhou 730000, China)
出处
《计算机系统应用》
2019年第8期71-77,共7页
Computer Systems & Applications
基金
甘肃省青年科技基金(2014GS03498)~~
关键词
深度学习
抗菌药物
问题分类
数据挖掘
注意力模型
deep learning
antibacterial drug
question classification
data mining
attention model
