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

TL;DR

This paper introduces SESA, a novel self-attention based structural equation modeling approach that dynamically adapts for improved missing data imputation in electronic health records, enhancing accuracy and robustness.

Contribution

SESA innovatively combines self-attention mechanisms with SEM to dynamically adapt and improve data imputation in complex EHR datasets, surpassing traditional static methods.

Findings

SESA achieves robust imputation performance in EHR datasets.

The architecture corrects SEM mis-specifications and integrates causal discovery.

Demonstrates broad applicability and improved accuracy over existing methods.

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.