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Clustering-Informed Shared-Structure Variational Autoencoder for Missing Data Imputation in Large-Scale Healthcare

Yasin Khadem Charvadeh1, Kenneth Seier1, Katherine S Panageas1

  • 1Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

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Summary
This summary is machine-generated.

We introduce a new method, the clustering-informed shared-structure variational autoencoder (CISS-VAE), to accurately impute missing data in electronic health records (EHR). This advanced technique improves healthcare analytics by handling complex data relationships and various missing data types.

Keywords:
electronic health recordsmissing data imputationmissing not at randomvariational autoencoder

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Area of Science:

  • Health Informatics
  • Machine Learning
  • Biostatistics

Background:

  • Missing data in electronic health records (EHR) and patient-reported outcomes hinders healthcare analytics.
  • Conventional imputation methods fail to capture complex nonlinear relationships and various missing data mechanisms, including missing not at random (MNAR).

Purpose of the Study:

  • To develop an advanced imputation method that effectively addresses the challenges of missing data in healthcare analytics.
  • To improve the accuracy and usability of EHR and patient-reported outcome data for health monitoring and analysis.

Main Methods:

  • Proposed the clustering-informed shared-structure variational autoencoder (CISS-VAE), a Bayesian neural network model.
  • Developed iterative learning algorithms to enhance imputation accuracy and prevent overfitting.
  • Validated the model through comprehensive simulations and application to real-world EHR data.

Main Results:

  • The CISS-VAE model demonstrated superior accuracy compared to traditional and contemporary imputation methods in simulations.
  • The model effectively captures complex associations and accommodates various missing data mechanisms, including MNAR.
  • Successful application to EHR data from early-stage breast cancer patients.

Conclusions:

  • The CISS-VAE model offers a powerful solution for mitigating the impact of missing data in healthcare analytics.
  • This approach enhances the reliability of health monitoring and analyses using EHR data.
  • The proposed method advances the field of health informatics by improving data imputation techniques.