201. A Multiview Model for Detecting the Inappropriate Use of Prescription Medication: Machine Learning Approach
- Author
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Shuang Tang, Yu Yang, Lin Zhuo, Yinchu Cheng, Liu Shaoqin, Siyan Zhan, Jiancun Zhen, and Junfeng Zhao
- Subjects
Topic model ,Matching (statistics) ,multiview learning ,Computer science ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Stability (learning theory) ,Early detection ,Health Informatics ,latent Dirichlet allocation ,Machine learning ,computer.software_genre ,Latent Dirichlet allocation ,03 medical and health sciences ,symbols.namesake ,0302 clinical medicine ,Health Information Management ,030212 general & internal medicine ,Medical diagnosis ,Medical prescription ,topic model ,030304 developmental biology ,Original Paper ,0303 health sciences ,business.industry ,inappropriate use of prescription medication ,Gold standard (test) ,prescription review ,symbols ,Artificial intelligence ,business ,computer - Abstract
Background The inappropriate use of prescription medication has recently garnered worldwide attention, but most national policies do not effectively provide for early detection or timely intervention. Objective This study aimed to develop and assess the validity of a model that can detect the inappropriate use of prescription medication. This effort combines a multiview and topic matching method. The study also assessed the validity of this approach. Methods A multiview extension of the latent Dirichlet allocation algorithm for topic modeling was chosen to generate diagnosis-medication topics, with data obtained from the Chinese Monitoring Network for Rational Use of Drugs (CMNRUD) database. Topic mapping allowed for calculating the degree to which diagnoses and medications were similarly distributed and, by setting a threshold, for identifying prescription misuse. The Beijing Regional Prescription Review Database (BRPRD) database was used as the gold standard to assess the model’s validity. We also conducted a sensitivity analysis using random samples of validated prescriptions and evaluated the model’s performance. Results A total of 44 million prescriptions were used to generate topics using the diagnoses and medications from the CMNRUD database. A random sample (15,000 prescriptions) from the BRPRD was used for validation, and it was found that the model had a sensitivity of 81.8%, specificity of 47.4%, positive-predictive value of 14.5%, and negative-predictive value of 96.0%. The model showed superior stability under different sampling proportions. Conclusions A method that combines multiview topic modeling and topic matching can detect the inappropriate use of prescription medication. This model, which has mediocre specificity and moderate sensitivity, can be used as a primary screening tool and will likely complement and improve the process of manually reviewing prescriptions.
- Published
- 2020