Characterizing the critical features when personalizing antihypertensive drugs using spectrum analysis and machine learning methods
作者：
Highlights：
• Spectrum analysis based on a statistical method and five algorithms based on machine learning were used to extract the critical clinical attributes.
• Critical attributes of five frequently used antihypertensive drugs (Irbesartan, Metoprolol, Felodipine, Amlodipine, and Levamlodipine) for hypertension control were extracted.
• Clinical analysis showed that the extracted important clinical attributes of the five drugs were both reasonable and meaningful in the personalization of hypertension treatment.
摘要
•Spectrum analysis based on a statistical method and five algorithms based on machine learning were used to extract the critical clinical attributes.•Critical attributes of five frequently used antihypertensive drugs (Irbesartan, Metoprolol, Felodipine, Amlodipine, and Levamlodipine) for hypertension control were extracted.•Clinical analysis showed that the extracted important clinical attributes of the five drugs were both reasonable and meaningful in the personalization of hypertension treatment.
论文关键词：Data mining methods,Antihypertensive drugs,Drug-related attributes,Blood pressure control,Machine learning
论文评审过程：Received 30 January 2019, Revised 10 December 2019, Accepted 28 February 2020, Available online 29 February 2020, Version of Record 4 March 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101841
