Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories

By Alexandre Yahi, Rami Vanguri, Noémie Elhadad, Nicholas P. Tatonetti. 2017

43 citations recorded by the source corpus.

Generative Adversarial Networks (GANs) represent a promising class of generative networks that combine neural networks with game theory. From generating realistic images and videos to assisting musical creation, GANs are transforming many fields of arts and sciences. However, their application to healthcare has not been fully realized, more specifically in generating electronic health records (EHR) data. In this paper, we propose a framework for exploring the value of GANs in the context of continuous laboratory time series data. We devise an unsupervised evaluation method that measures the predictive power of synthetic laboratory test time series. Further, we show that when it comes to predicting the impact of drug exposure on laboratory test data, incorporating representation learning of the training cohorts prior to training GAN models is beneficial.

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1. Zilong Zhao
2. Aditya Kunar
3. H. V. D. Scheer
4. Robert Birke
5. L. Chen
6. Lei Xu
7. Lei Xu
8. Maria Skoularidou

Citations in the corpus

• Synthesizing Tabular Data using Generative Adversarial Networks (2018, background): “RGAN and RCGAN [13] can generate real-valued time-series data. 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…”
• Modeling Tabular data using Conditional GAN (2019, methodology): “GANs are also used in generating tabular data, especially healthcare records; for example, [25] uses GANs to generate continuous time-series medical records and [4] proposes the generation of discrete tabular data using GANs.”
• Generation of Synthetic Electronic Medical Record Text (2018, methodology): “Yahi utilized a GAN framework to produce continuous time series data in EMRs, which can predict the effects of drug exposure [14].”
• CTAB-GAN: Effective Table Data Synthesizing (2021, methodology): “GANs are a popular method to generate synthetic data first applied with great success to images [10, 11] and later adapted to tabular data [26].”