Globally, methods of controlling blood pressure in hypertension patients remain inefficient. The difficulty of prescribing appropriate drugs specific to a patient’s clinical features serves as one of the most important factors. Characterizing the critical drug-related features, just like that of the antibacterial spectrum (where each item is sensitive to the targeted drug’s effectiveness or a specified indication), may help a doctor easily prescribe appropriate drugs by matching a patient’s attributes with drug-related features, and effectiveness of the selected drugs would also be ascertained. In this study, we aimed to apply data mining methods to obtain the clinical characteristics spectrum or important clinical features of five frequently used drugs (Irbesartan, Metoprolol, Felodipine, Amlodipine, and Levamlodipine) for hypertension control by comparing successful and unsuccessful cases. Spectrum analysis based on a statistical method and five algorithms based on machine learning were used to extract the critical clinical features. A visualized relative weight matrix was then achieved by combining the results from the characteristic spectrum and machine learning-based methods. Our results indicated that the five targeted antihypertension agents had different importance orders of the 15 relative clinical features. Clinical analysis showed that the extracted important clinical attributes of the five drugs were both reasonable and meaningful in the selection of hypertension treatment. Therefore, our study provided a data-driven reference for the personalization of clinical antihypertensive drugs.

在全球范围内，控制高血压患者血压的方法仍然效率低下。针对患者的临床特征开出合适的药物的难度是最重要的因素之一。表征关键的药物相关特征，就像抗菌谱（其中每个项目对靶向药物的有效性或指定的适应症敏感）一样，可以通过将患者的属性与药物相关的特征进行匹配来帮助医生轻松地开出合适的药物，并且还将确定所选药物的有效性。在本研究中，我们旨在应用数据挖掘方法获得五种常用药物（厄贝沙坦、美托洛尔、非洛地平、氨氯地平、和左旋氯地平）通过比较成功和不成功的案例来控制高血压。使用基于统计方法的频谱分析和基于机器学习的五种算法来提取关键的临床特征。然后通过结合特征谱和基于机器学习的方法的结果来实现可视化的相对权重矩阵。我们的结果表明，五种靶向降压药在 15 个相关临床特征中具有不同的重要性顺序。临床分析表明，提取的5种药物的重要临床属性对高血压治疗的选择既合理又有意义。因此，我们的研究为临床降压药物的个性化提供了数据驱动的参考。使用基于统计方法的频谱分析和基于机器学习的五种算法来提取关键的临床特征。然后通过结合特征谱和基于机器学习的方法的结果来实现可视化的相对权重矩阵。我们的结果表明，五种靶向降压药在 15 个相关临床特征中具有不同的重要性顺序。临床分析表明，提取的5种药物的重要临床属性对高血压治疗的选择既合理又有意义。因此，我们的研究为临床降压药物的个性化提供了数据驱动的参考。使用基于统计方法的频谱分析和基于机器学习的五种算法来提取关键的临床特征。然后通过结合特征谱和基于机器学习的方法的结果来实现可视化的相对权重矩阵。我们的结果表明，五种靶向降压药在 15 个相关临床特征中具有不同的重要性顺序。临床分析表明，提取的5种药物的重要临床属性对高血压治疗的选择既合理又有意义。因此，我们的研究为临床降压药物的个性化提供了数据驱动的参考。