一种概率生成模型,用于发现缺少数据的共存疾病的治疗方法
Onintze Zaballa1, Aritz Pérez1, Elisa Gómez-Inhiesto2
1BCAM-Basque Center for Applied Mathematics, Bilbao, 48009, Bizkaia, Spain.
Computer methods and programs in biomedicine
|November 17, 2023
概括
这项研究引入了一种新的模型,以了解使用电子健康记录 (EHR) 的患者疾病如何共同演变. 该模型细分患者病史,学习疾病模式,并识别患者亚型,即使缺少诊断数据.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 伴随性疾病,或同时存在的疾病,在患者中表现出复杂的时间模式.
- 从电子健康记录 (EHR) 中了解疾病的共同演变至关重要.
- 现有的方法与电子健康记录中缺少诊断数据的常见问题作斗争.
研究的目的:
- 提出一种新的概率生成模型,用于分析疾病的共同进化.
- 为了应对电子健康记录中不完整的诊断信息的挑战.
- 细分患者病史,学习特定疾病的模型,并根据并发症模式发现患者亚型.
主要方法:
- 使用隐性结构模型,将患者分配到代表并发症演变的隐性类别.
- 每一个医疗事件都与潜在的疾病有关.
- 一个预期最大化算法,通过动态编程优化,用于高效的学习.
主要成果:
- 在合成数据实验中,生成模型成功地恢复了基础数据生成过程.
- 对现实世界电子病历数据的实验证明了病史的准确细分.
- 该模型实现了精确的患者亚型和有效的诊断归算.
结论:
- 提出了一个可解释的生成模型来管理不完整的EHR数据.
- 该模型有效地描述了基于活跃的共同疾病的共存疾病的共同演变.
- 这种方法增强了对个体患者复杂疾病动态的理解.
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