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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...

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TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

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Electronic medical records imputation by temporal Generative Adversarial Network.

Yunfei Yin¹, Zheng Yuan², Islam Md Tanvir²

¹College of Computer Science, Chongqing University, Chongqing, 400044, China. yinyunfei@cqu.edu.cn.

|June 26, 2024

Summary

This study introduces UGAN-GRUD, an improved method for filling missing electronic medical record data. UGAN-GRUD significantly enhances imputation accuracy, especially for datasets with high missing rates.

Keywords:

Association relation Electronic medical record Generative adversarial networks Missing value Time decay

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Area of Science:

Biomedical Informatics
Machine Learning
Data Science

Background:

Loss of electronic medical records hinders biomedical data analysis.
Current Generative Adversarial Network (GAN) methods struggle with high missing data rates.

Purpose of the Study:

To improve imputation accuracy for electronic medical records with high missing rates.
To develop a novel method addressing GAN limitations in data imputation.

Main Methods:

Proposed UGAN-GRUD, combining Uncertainty Generative Adversarial Network (UGAN) and Gate Recurrent Unit Decay (GRUD).
UGAN learns data distribution and uncertainty; GRUD compensates using time decay for temporal relations.
Designed and integrated UGAN and GRUD networks iteratively.

Main Results:

UGAN-GRUD demonstrated superior performance over state-of-the-art methods on public biomedical datasets.
Achieved average improvements of 13% in Root Mean Squared Error (RMSE) and 24.5% in Mean Absolute Percentage Error (MAPE).

Conclusions:

UGAN-GRUD effectively addresses the challenge of imputing missing electronic medical record data.
The proposed method offers a significant advancement for biomedical data integrity and application.