开发和验证用于不遵守抗高血压药物的预测模型
Cristian Daniel Marineci1, Andrei Valeanu1, Cornel Chiriță1
1Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020956 Bucharest, Romania.
Medicina (Kaunas, Lithuania)
|July 30, 2025
概括
这项研究使用人工智能 (AI) 来预测高血压药物的低坚持. 机器学习模型识别了需要干预的患者,尽管性能适度,显示AI.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 低于最佳的抗高血压药物的坚持影响治疗结果.
- 人工智能 (AI) 提供了分析药物坚持数据的先进功能.
- 有针对性的干预措施需要准确识别具有较低粘附度的患者.
研究的目的:
- 开发和验证对抗高血压药物不服药的预测模型.
- 利用患者报告的数据和机器学习来预测坚持.
- 为了确定与高血压患者的药物坚持相关的关键因素.
主要方法:
- 一项涉及3095名来自社区药房的高血压患者的横截面研究.
- 通过对社会人口统计学,医学和行为因素的结构化问卷收集数据.
- 开发和验证五种机器学习模型 (物流回归,随机森林,CatBoost,LightGBM,XGBoost) 以使用补充和药物依赖度 (ARMS) 预测不遵守.
主要成果:
- 观察到低于最佳的坚持率很高 (79.13%的ARMS分数≥15).
- 与更好的坚持相关的因素包括频繁的血压自我监测,减少盐摄入量和药剂师信息.
- 在评估的机器学习模型中,CatBoost模型表现最高,ROC AUC得分不超过0.75.
结论:
- 机器学习模型已成功开发和验证,用于估计药物不坚持水平.
- 人工智能显示出潜在的识别和分层患者基于坚持的个人资料,尽管适度的模型性能.
- 这项研究开创了 permutation 的使用和 SHapley 添加式 exPlanations 的功能重要性,与基于概率的坚持分层用于预测建模.
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