计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211100241-9.doi: 10.11896/jsjkx.211100241
霍甜媛, 顾晶晶
HUO Tian-yuan, GU Jing-jing
摘要: 疾病诊断是电子健康记录数据挖掘的热门研究领域,也是实现医疗诊断智能化的一个重要环节。但是,电子健康记录中健康感知数据的来源多样、数据结构复杂,且不同类型的数据之间有着潜在的相关性,在进行特征提取和挖掘分析过程中存在着异构数据应该如何融合的问题。只有对医学感测数据、个人体质记录数据、疾病间关系数据进行综合考虑,挖掘其中的相关隐藏特征,才能对多种类别疾病进行更准确的诊断。因此,基于多源健康感知数据动静态关系融合的疾病诊断模型(DSRF)首先通过动静态关系融合算法解决动态医学感测数据和静态体质记录数据的异构性问题并挖掘其相关关系,然后计算多类别疾病的关联矩阵来提取疾病间依赖关系,最后在门控循环单元网络架构的基础上将多种健康感知数据进行融合,完成了多源异构数据的综合分析。在美国MIMIC-III临床数据集上的实验结果证明,相比同类型主流模型,该模型可以更准确地对多种类别疾病进行联合诊断。
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