吉林大学学报(信息科学版) ›› 2026, Vol. 44 ›› Issue (3): 642-648.

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电子病历多模态数据缺失值动态填补算法

马 鸣1, 吴天智2, 周 强3, 罗 维4   

  1. 1. 四川省中医药科学院信息统计处,成都610041;2. 四川省妇女儿童医院 信息化部,成都610045; 3. 成都慧康投资发展有限责任公司科技创新发展中心,成都611230;4. 成都中医药大学智能医学学院,成都611137
  • 收稿日期:2025-06-13 出版日期:2026-06-02 发布日期:2026-06-02
  • 作者简介:马鸣(1981— ), 男, 四川崇州人, 四川省中医药科学院工程师,主要从事智慧医院智能化转型路径、健康医疗数据标准 化体系构建研究,(Tel)86-18980447200(E-mail)Ma_ming99@163. com。
  • 基金资助:
    四川省自然科学基金资助项目(2022NSFSC1578); 四川省科技支撑计划基金资助项目(2011SZ0250)

 Dynamic Filling Algorithm for Missing Values in Multimodal Data of Electronic Medical Records

MA Ming1, WU Tianzhi2, ZHOU Qiang3, LUO Wei4   

  1. 1. Information Statistics Department, Sichuan Academy of Chinese Medicine Sciences, Chengdu 610041, China; 2. Department of Information Technology, Sichuan Provincial Women’s and Children’s Hospital, Chengdu 610045, China; 3. Science and Technology Innovation Development Center, Chengdu Huikang Investment Development Limited Liability Company, Chongzhou 611230, China; 4. School of Intelligent Medical, Chengdu University of TCM, Chengdu 611137, China
  • Received:2025-06-13 Online:2026-06-02 Published:2026-06-02

摘要: 针对在电子病历数据进行填充时使用固定的填充值会导致填补后的数据完整度不高影响填补方法质量的问题提出电子病历多模态数据缺失值动态填补算法。将获取的电子病历多模态数据进行融合构建电子病历数据模型并从中挖掘出相应的序列特征在前向传播网络的作用下检测电子病历数据的缺失值位置。根据建立的时间序列矩阵对电子病历数据缺失值进行估计并将估计的缺失值作为初始值对电子病历数据进行初步填充。应用模糊聚类算法对其进行初步聚类并通过不断更新聚类中心和填补值计算出最优的填补值, 以实现对电子病历数据缺失值的填充。实验结果表明设计的填补算法能对电子病历数据进行准确填充, 且填充后的电子病历数据完整度为0.95, 填补后的数据质量较高。

关键词: 电子病历, 多模态数据, 数据缺失值, 缺失值填补, 填补算法, 缺失值检测

Abstract: When filling electronic medical record data, a fixed filling value will result in low completeness of the filled data, affecting the quality of the filling method. Therefore a dynamic filling algorithm for missing values in multimodal data of electronic medical records is proposed. The obtained multimodal data of electronic medical records is fused, an electronic medical record data model is constructed, corresponding sequence features are mined, and the missing value positions of electronic medical record data is detected under the action of forward propagation network. The missing values of electronic medical record data is estimated based on the established time series matrix. Using the estimated missing values as initial values, the electronic medical record data is preliminarily filled in, and then the fuzzy clustering algorithm is applied to perform preliminary clustering. By continuously updating the clustering centers and filling values, the optimal filling value is calculated to achieve the filling of missing values in the electronic medical record data. The experimental results show that the designed filling algorithm can accurately fill in electronic medical record data, and the completeness of the filled electronic medical record data is 0. 95, indicating high data quality.

Key words: electronic medical records, multimodal data, data missing values, fill in missing values, filling algorithm, missing value detection

中图分类号: 

  • TP311.13