Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records

By Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, Yan Liu. 2017

180 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Information Management, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Synthesizing Tabular Data using Generative Adversarial Networks (2018, background): “ta. medGAN [9], corrGAN [33] and several improved models [2, 6, 47, 3, 44] can generate discrete medical records but do not tackle the complexity in generating multimodal continuous variables. ehrGAN [8] generates augmented medical records but doesn’t explicitly generate synthetic data. Finally, per”
Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review (2018, methodology): “The goal of these models is to embed terminologies from different domains into the same space to discover the relations among them (eg., NA [33, 35, 98, 89, 56, 98] [34] relationships between diseases and drugs).”
Federated Learning for Smart Healthcare: A Survey (2021, background): “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.”
Modeling Tabular data using Conditional GAN (2019, unknown): “ehrGAN [5] generates augmented medical records.”
An empirical survey of data augmentation for time series classification with neural networks (2020, background): “GAN [92,93,94]”
Synthetic data generation for tabular health records: A systematic review (2022, methodology): “Che 2017 [35] Categorical Time-series 3357 6714 6744 ehrGAN* Excellent Excellent -”
Deep learning for time series classification: a review (2018, methodology): “Other ESN-based approaches (Chen et al., 2015a, 2013; Che et al., 2017b) define a kernel over the learned representation followed by an SVM or an MLP classifier.”
Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification (2018, background): “are starting to emerge, demonstrating improved performance for the disease prediction [94] and risk prediction given treatment [95].”