在精神分裂症和减弱精神病障碍患者中,基于人工智能的药物坚持预测
Zheng Zhu1, Dooti Roy1, Shaolei Feng2
1Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, CT 06877, USA.
Schizophrenia research
|December 5, 2024
概括
机器学习使用智能手机数据准确地预测药物坚持. 较长的监测时间和较低的坚持值提高了预测准确度,有助于临床干预.
科学领域:
- 数字健康数字健康
- 机器学习在医学中的应用
- 临床试验方法论 临床试验方法论
背景情况:
- 药物坚持是治疗疗效的关键.
- 准确预测坚持可以优化患者的结果.
- 智能手机应用程序为监控遵守提供了新的方法.
研究的目的:
- 评估机器学习算法来预测药物坚持.
- 使用计算机视觉辅助智能手机应用程序 (AiCure) 的数据优化机器学习模型.
- 为了评估模型在不同监测持续时间,坚持截止时间和时间点上的性能.
主要方法:
- 利用计算机视觉辅助智能手机应用程序AiCure的数据来记录药物摄入.
- 优化了一种机器学习模型,以使用两个II期试验 (精神分裂症和减弱精神病障碍) 的数据来预测试验坚持.
- 对具有不同参数的模型进行10次交叉验证的分析曲线下面面积 (AUC),错误负面率和错误遗漏率 (7/10/14天监测,0.6/0.7/0.8坚持截止点,开始/中期/结束时间点).
主要成果:
- 在临床试验结束时的14天监测期中,依从度截止值为0.6,产生了最准确的预测 (AUC:0.92).
- 在60.4%的患者中观察到更高的坚持率 (≥80%).
- 与安慰剂相比,在NCT03351244的后期分析显示,预测依附 (HR=0.510) 的患者的第一次复发风险较低.
结论:
- 较长的监测持续时间 (14天),较低的坚持截止值 (0.6),以及较晚的时间点 (试验结束) 会导致最准确的坚持预测.
- 机器学习模型可以有效地预测药物坚持.
- 准确的坚持预测可以为临床策略提供信息,以改善患者的坚持和结果.
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