Publication

Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit

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Last modified
  • 05/24/2025
Type of Material
Authors
    Yuandu Zhu, School of Electrical and Computer EngineeringJanani Venugopalan, Emory UniversityZhenyu Zhang, Georgia Institute of TechnologyNikhil Chanani, Emory UniversityKevin Maher, Emory UniversityDongmei Wang, Emory University
Language
  • English
Date
  • 2022-04-11
Publisher
  • Frontiers Media S.A
Publication Version
Copyright Statement
  • © 2022 Zhu, Venugopalan, Zhang, Chanani, Maher and Wang.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 5
Start Page
  • 640926
End Page
  • 640926
Grant/Funding Information
  • This project was supported in part by the Children's Healthcare of Atlanta (CHOA), the NIH National Center for Advancing Translational Sciences UL1TR000454, the National Science Foundation Award NSF1651360, Microsoft Research, Georgia Institute of Technology PACE, and Hewlett Packard.
Abstract
  • More than 5 million patients have admitted annually to intensive care units (ICUs) in the United States. The leading causes of mortality are cardiovascular failures, multi-organ failures, and sepsis. Data-driven techniques have been used in the analysis of patient data to predict adverse events, such as ICU mortality and ICU readmission. These models often make use of temporal or static features from a single ICU database to make predictions on subsequent adverse events. To explore the potential of domain adaptation, we propose a method of data analysis using gradient boosting and convolutional autoencoder (CAE) to predict significant adverse events in the ICU, such as ICU mortality and ICU readmission. We demonstrate our results from a retrospective data analysis using patient records from a publicly available database called Multi-parameter Intelligent Monitoring in Intensive Care-II (MIMIC-II) and a local database from Children's Healthcare of Atlanta (CHOA). We demonstrate that after adopting novel data imputation on patient ICU data, gradient boosting is effective in both the mortality prediction task and the ICU readmission prediction task. In addition, we use gradient boosting to identify top-ranking temporal and non-temporal features in both prediction tasks. We discuss the relationship between these features and the specific prediction task. Lastly, we indicate that CAE might not be effective in feature extraction on one dataset, but domain adaptation with CAE feature extraction across two datasets shows promising results.
Author Notes
Keywords
Research Categories
  • Engineering, Biomedical
  • Engineering, Electronics and Electrical

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