国内已上市辅助用药数据人工智能挖掘分析方法研究

Research on Artificial Intelligence Data Mining Analysis Method of Adjuvant Drugs in China

目的:利用自然语言处理技术挖掘超说明书用药方法。方法:本研究使用自身建立的药品说明书数据库训练深度学习模型(双向长短期记忆网络结合条件随机场模型,Bi-directional Long Short-Term Memory Network-CRF,BiLSTM-CRF)识别疾病实体,结合国家人口与健康科学数据共享服务平台提供的数据寻找超说明书用药,利用Meta分析验证相应的辅助用药合理性。结果:利用深度学习模型实现了84.19%的F值,得到47种疑似超说明书用药,人工筛选得10种超说明书用药,Meta分析验证了参麦注射液用于急性心肌梗死的合理性。结论:本研究有效提取了药品说明书中的疾病实体,证明了自然语言处理在医药领域的非结构化数据转化为结构化数据过程中起着重大作用,对发现超说明书用药、促进合理用药具有重要意义。

Objective:Using natural language processing(NLP)technology to analysis off-label drug use.Methods:The study used self-established drug instruction database to train deep learning model(Bi-directional Long Short-Term Memory Network-CRF,BiLSTM-CRF)to identify disease entities,combined with the data provided by National Population and Health Science Data Sharing Service Platform to find the off-label drug use,and applied Meta-analysis to verify the rationality of the corresponding adjuvant medication.Results:The F-score of the BiLSTM-CRF model was 84.19%.The results showed that 47 kinds of suspected off-label drug use were obtained,and 10 kinds of off-label drug use were found by manual screening.Meta-analysis verified the rationality of Shenmai injection in adjurant treatment of acute myocardial infarction.Conclusion:This study effectively extracted the disease entity in the drug label,and proved that NLP played a major role in the transformation of the medical data from unstructured data into structured data,which is of great significance for discovering the off-label drug use and promoting rational drug use.