Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention

Ou Deng, Qun Jin

TL;DR

SESA addresses missing data in electronic health records by integrating a dynamically adaptable structural equation model with a self-attention module for imputation, framed to maximize mutual information between observed and missing values. It combines SEM-based latent structure with a transformer-style attention mechanism, using FIML for initialization and joint optimization via a composite loss that includes MSE, covariance preservation, and sparsity. Causal discovery via NOTEARS guides SEM initialization, and experiments on a CDC BRFSS-derived dataset show that SESA outperforms a range of baselines across sample sizes and variable types, with robust performance and meaningful improvements in imputation quality. This work advances healthcare data analytics by uniting statistical modeling and deep representation learning, offering a scalable and adaptable approach with potential extensions to nonlinear and temporal causal modeling.

Abstract

Addressing missing data in complex datasets including electronic health records (EHR) is critical for ensuring accurate analysis and decision-making in healthcare. This paper proposes dynamically adaptable structural equation modeling (SEM) using a self-attention method (SESA), an approach to data imputation in EHR. SESA innovates beyond traditional SEM-based methods by incorporating self-attention mechanisms, thereby enhancing model adaptability and accuracy across diverse EHR datasets. Such enhancement allows SESA to dynamically adjust and optimize imputation and overcome the limitations of static SEM frameworks. Our experimental analyses demonstrate the achievement of robust predictive SESA performance for effectively handling missing data in EHR. Moreover, the SESA architecture not only rectifies potential mis-specifications in SEM but also synergizes with causal discovery algorithms to refine its imputation logic based on underlying data structures. Such features highlight its capabilities and broadening applicational potential in EHR data analysis and beyond, marking a reasonable leap forward in the field of data imputation.

Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention

TL;DR

SESA addresses missing data in electronic health records by integrating a dynamically adaptable structural equation model with a self-attention module for imputation, framed to maximize mutual information between observed and missing values. It combines SEM-based latent structure with a transformer-style attention mechanism, using FIML for initialization and joint optimization via a composite loss that includes MSE, covariance preservation, and sparsity. Causal discovery via NOTEARS guides SEM initialization, and experiments on a CDC BRFSS-derived dataset show that SESA outperforms a range of baselines across sample sizes and variable types, with robust performance and meaningful improvements in imputation quality. This work advances healthcare data analytics by uniting statistical modeling and deep representation learning, offering a scalable and adaptable approach with potential extensions to nonlinear and temporal causal modeling.

Abstract

Addressing missing data in complex datasets including electronic health records (EHR) is critical for ensuring accurate analysis and decision-making in healthcare. This paper proposes dynamically adaptable structural equation modeling (SEM) using a self-attention method (SESA), an approach to data imputation in EHR. SESA innovates beyond traditional SEM-based methods by incorporating self-attention mechanisms, thereby enhancing model adaptability and accuracy across diverse EHR datasets. Such enhancement allows SESA to dynamically adjust and optimize imputation and overcome the limitations of static SEM frameworks. Our experimental analyses demonstrate the achievement of robust predictive SESA performance for effectively handling missing data in EHR. Moreover, the SESA architecture not only rectifies potential mis-specifications in SEM but also synergizes with causal discovery algorithms to refine its imputation logic based on underlying data structures. Such features highlight its capabilities and broadening applicational potential in EHR data analysis and beyond, marking a reasonable leap forward in the field of data imputation.
Paper Structure (28 sections, 7 equations, 2 figures, 3 tables, 1 algorithm)

This paper contains 28 sections, 7 equations, 2 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Theoretical Analysis
  5. SESA Core Functions
  6. Structural Equation Modeling (SEM)
  7. Full Information Maximum Likelihood (FIML)
  8. Self-Attention Mechanism
  9. Loss Function
  10. Optimization and Inference
  11. Algorithmic Implementation
  12. Experiment
  13. Experimental Setup
  14. Data Source and Experimental Dataset
  15. Variable Selection and Data Characteristics
  16. ...and 13 more sections

Figures (2)

  • Figure 1: Distribution of the selected variables in the experimental dataset. Data is normalized within the respective ranges of variables.
  • Figure 2: Causal discovery analysis results of for the selected variables group by NOTEARS algorithm. In this context, nodes represent the variables included in the dataset, while directed edges indicate potential causal directions. The weight of each directed edge reflects the strength of the causal effect.