使用基于真实世界的证据的机器学习技术,预测抑郁症患者西的血度
Lin Yang1,2, Jinyuan Zhang3, Jing Yu1,2
1Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Expert review of clinical pharmacology
|July 19, 2023
概括
这项研究使用机器学习来预测抑郁症患者的奎蒂阿平水平,帮助临床决策. 开发的模型实现了75.47%的准确度,用于预测治疗度.
科学领域:
- 药物基因组学 药物基因组学
- 临床药理学 临床药理学
- 人工智能在医学中的应用
背景情况:
- 奎蒂亚平是一种广泛处方的非典型抗精神病药物,用于治疗抑郁症.
- 治疗药物监测 (TDM) 对于优化奎胺剂量和最大限度地减少不良影响至关重要.
- 预测建模可以提高提阿平的TDM精度.
研究的目的:
- 开发和验证一种机器学习模型,用于预测患有抑郁症的患者的西血度.
- 为了支持临床决策,关于提阿治疗方案.
主要方法:
- 分析了333名抑郁症住院患者的队列,数据分为80:20培训和测试集.
- 单变量分析确定了影响quetiapine TDM的关键变量.
- 评估了9个机器学习和深度学习算法,CatBoost被选为模型开发,SHapley Additive exPlanation被选为解释.
主要成果:
- 通过使用CatBoost算法建立了夸他平度的预测模型.
- 测试队列中的整体预测准确率为61.45%.
- 该模型在预测有效治疗范围 (200-750 ng/mL) 内的度方面表现出75.47%的准确性.
结论:
- 在抑郁症患者中基于机器学习预测奎平的血度是可行的.
- 开发的模型为指导奎平的临床药物管理提供了有价值的工具.
- 这种方法有可能改善治疗结果,并个性化治疗抑郁症.
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