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Autoencoder-Based Representation Learning for Similar Patients Retrieval From Electronic Health Records: Comparative

Deyi Li¹, Aditi Shukla², Sravani Chandaka³

¹Department of Health Outcomes & Biomedical Informatics, University of Florida, 1889 Museum Rd, 7th Floor, Suite 7000, Room 7012, Gainesville, FL, 32611, United States, 1 352-627-9143.

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Summary

This summary is machine-generated.

Denoising autoencoders excel at finding similar patients using Euclidean distance, outperforming other models. Learning rates and distance measures significantly impact performance in patient representation learning for personalized medicine.

Keywords:

decision support for health professionals electronic health records machine learning methods and instruments in medical informatics

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Area of Science:

Medical informatics
Machine learning in healthcare
Computational biology

Background:

Electronic health record (EHR) data modeling is challenging due to high dimensionality, mixed features, noise, bias, and sparsity.
Patient representation learning using autoencoders (AEs) offers a promising approach to address these EHR data challenges.
Understanding the impact of different AE designs and distance measures on retrieving similar patient cohorts is crucial.

Purpose of the Study:

To evaluate the performance of five common autoencoder (AE) variants in retrieving similar patients.
To investigate the influence of various distance measures and hyperparameter configurations on AE model performance.
To assess the effectiveness of AE-based patient similarity estimation for clinical outcome prediction.

Main Methods:

Tested five AE variants (vanilla, denoising, contractive, sparse, robust) on two real-world EHR datasets.
Applied k-nearest neighbors (k-NN) with Euclidean and Mahalanobis distances to AE-produced latent representations.
Evaluated model performance on predicting acute kidney injury onset and 1-year postdischarge mortality.

Main Results:

Denoising autoencoders significantly outperformed other AE variants when paired with Euclidean distance (P<.001).
Learning rates were identified as a critical hyperparameter influencing AE model performance.
Mahalanobis distance-based k-NN frequently outperformed Euclidean distance-based k-NN on latent representations, though direct application to raw data was data-dependent.

Conclusions:

This study provides a comprehensive analysis of AE variants for patient similarity retrieval.
Findings highlight the importance of AE design and hyperparameter tuning for effective patient representation learning.
The results lay the groundwork for developing advanced AE-based methods for personalized medicine and improved patient care.