针对慢性疾病的个性化药物使用多模式数据驱动的决策链
Xiaoli Chu1, Yiheng Ye2, Siqiao Tang3
1State Key Laboratory of Traditional Chinese Medicine Syndrome/Big Data Research Center of Chinese Medicine, The 2nd Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong, 510120, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|August 11, 2025
概括
一个新的多式数据驱动决策链 (MDD-CoD) 框架增强了慢性疾病的个性化药物治疗. 它将患者数据与药物特性集成在一起,改善治疗决策和治疗结果.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
- 药物基因组学 药物基因组学
背景情况:
- 个性化药物治疗对于慢性疾病管理至关重要,但它面临着整合各种患者和药物数据的挑战.
- 目前的模型通常依赖于有限的数据类型 (临床或分子),阻碍了全面的患者药物关系建模.
- 顺序性决策过程是临床实践中固有的,用于确定最佳的药物治疗方案.
研究的目的:
- 为个性化药物提供一个新的多模式数据驱动决策链 (MDD-CoD) 框架.
- 将多模式临床表型数据,多属性药物数据和专家见解整合到一个连贯的决策过程中.
- 提高慢性疾病个性化药物推的准确性和可解释性.
主要方法:
- 开发了一个三阶段的深度学习框架 (MDD-CoD),模仿专家的临床决策.
- 纳入多式联络患者数据 (表型) 和药物属性 (宏观和微观层面).
- 验证了来自多家医院的四种慢性疾病 (CKD,MN,RA,CRC,KOA) 的五个数据集的框架.
主要成果:
- 与基线模型相比,MDD-CoD框架在个性化药物决策中表现出优异的预测性能.
- 通过将个体患者的特征与综合药物特性相结合,实现了更高的准确性.
- 该模型在跨疾病个性化决策任务中显示出更好的概括性和可解释性.
结论:
- MDD-CoD框架为慢性疾病的个性化药物提供了一个可扩展和有效的解决方案.
- 这种基本模型通过利用多式联络数据和决策链方法来推进临床决策支持.
- 该框架有望通过更精确和个性化的治疗策略来改善患者的治疗结果.
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