Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
Abstract
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in healthcare academia and industry, but most of these methods rely on massive labeled data. In this work, we propose a general deep learning framework which is able to boost risk prediction performance with limited EHR data. Our model takes a modified generative adversarial network namely ehrGAN, which can provide plausible labeled EHR data by mimicking real patient records, to augment the training dataset in a semi-supervised learning manner. We use this generative model together with a convolutional neural network (CNN) based prediction model to improve the onset prediction performance. Experiments on two real healthcare datasets demonstrate that our proposed framework produces realistic data samples and achieves significant improvements on classification tasks with the generated data over several stat-of-the-art baselines.
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Data augmentation techniques such as GANs may still not have good diversity to build a comprehensive dataset for efficient data training.
“One can use data augmentation techniques such as generative adversarial networks (GANs) [28] to solve these issues, but it may still not have good diversity to build a comprehensive dataset for efficient data training.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [28]
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“…adaptation methods that leverage distinct latent domains in the target environment 43 , data augmentation including the use of adversarial examples 44 , similarities between the source and target environments 45,46 , and semi-supervised learning that alternatively generate pseudo labels and…”
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- Authors
- Che, Z., Cheng, Y., Zhai, S., Sun, Z., & Liu, Y.
- Affiliations
- University of Southern California (US)
- IBM (United States) (US)
- Binghamton University (US)
- IBM Research - Thomas J. Watson Research Center (US)
- Venue
- 2017 IEEE International Conference on Data Mining (ICDM)
- Publisher
- IEEE
- Published
- 2017-11
- Pages
- 787-792
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2963034797
- Cited by
- 162
- References
- 42
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 18 · 2023: 22 · 2022: 23 · 2021: 28 · 2020: 29 · 2019: 14 · 2018: 8 · 2017: 4
- FWCI
- 6.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- electronic health records, risk prediction, generative adversarial networks, semi-supervised learning, data augmentation, limited labeled data, convolutional neural networks, onset prediction
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