Healthcare Cramér Generative Adversarial Network (HCGAN)

Indhumathi, R.; Devi, S. Sathiya

doi:10.1007/s10619-021-07346-x

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…The discriminator, in turn, attempts to distinguish the synthetic outputs from the ground truth. In the context of healthcare, Indhumathi et al propose the Healthcare Cramér Generative Adversarial Network (HCGAN) [19], which generates synthetic health data while preserving privacy and withstanding adversarial attacks. The results demonstrate the effectiveness of HCGAN in generating high-quality synthetic data in the healthcare domain.…”

Section: Adversarial Reinforcement Learning Techniquesmentioning

confidence: 99%

DLGNet: Adversarial Reinforcement Learning for Disease Label Generation

Yang

Chen

Wang

et al. 2023

Preprint

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International Classification of Diseases (ICD) coding has been considered as a multi-label prediction problem, requiring the assignment of one or more codes to a detailed discharge summary. Existing automatic ICD coding algorithms struggle to effectively classify medical diagnosis texts representing deep sparse categories. We propose Disease Label Generation Network (DLGNet), a novel adversarial network that transforms ICD codes into a label generation challenge. This strategy faces three major challenges: (1) How to extract the relationship between clinical text and ICD codes? (2) What training methods should be used to improve the generalization and effectiveness of network? (3) How to evaluate the quality of disease labels generated by the DLGNet? For (1), we develop an information integration module (MIM) to encode the relationship between clinical text and ICD codes. For (2), we present adversarial training algorithms, such as reinforcement causal learning and adversarial perturbation regularization. For (3), we present a Label Discriminator (LD) that calculates the reward for each ICD code in the Label Generator (LG). In conclusion, DLGNet outperforms existing state-of-the-art approaches on evaluation measures such as micro-F1, leading to the formation of a new SOTA. The code is available at anonymous github link: https://anonymous.4open.science/r/DLGNet-787D.

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Section: Adversarial Reinforcement Learning Techniquesmentioning

confidence: 99%

DLGNet: Adversarial Reinforcement Learning for Disease Label Generation

Yang

Chen

Wang

et al. 2023

Preprint

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“…So that Cramér distance outperformed Wasserstein distance in terms of verifying the property of unbiased sample gradients. From this point, Cramér GAN (Bellemare et al, 2017), and HCGAN (Indhumathi & Devi, 2021) used Cramér distance and achieved more stability and diversity of the generated samples than WGAN. Also, EM‐GAN (Jin et al, 2020) used

L_{2}

‐norm distance (Ye et al, 2018) to speed up the convergence in the training process and provide better inference accuracy than the original GAN.…”

Section: Distributions Distance Metrics and Gansmentioning

confidence: 99%

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Mohammed

2023

WIREs Computational Stats

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In machine learning, a generative model is responsible for generating new samples of data in terms of a probabilistic model. Generative adversarial network (GAN) has been widely used to generate realistic samples in different domains and outperforms its peers in the generative models family. However, producing a robust GAN model is not a trivial task because many challenges face the GAN during the training process and impact its performance, affecting the quality and diversity of the generated samples. In this article, we conduct a comprehensive review of GANs to present the fundamentals of GAN, including its components, types, and objective functions. Also, we present an overview of the evaluation matrices used to evaluate GAN models. Moreover, we list the applications of GANs and research work in various domains. Finally, we present the challenges that face GANs and highlight two significant issues, representing mode collapse and training instability, in addition to those research efforts that tackle these challenges.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks

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“…The proposed framework, privacy-preserving Augmentation and Releasing scheme for Time series data via GAN (PART-GAN) uses weight pruning and grouping, generator selecting, and denoising mechanisms for improving the quality in time-series data [82]. Some works combined both theoretical and empirical evaluations to prove the privacy-preservation of the GAN model [85]. To avoid compromising the synthetic data fidelity, the authors applied partial differential privacy to the Quasi Identifier features; these features are then recombined with the other sensitive attributes.…”

Section: Privacy Preservationmentioning

confidence: 99%

“…The other type of attacks, namely model inversion refers to the scenario where an attacker aims to reconstruct the training data by their ability to constantly query the model [146], as shown in Figure 4 (c). This kind of attack was not frequently used in GANs for EHRs evaluation [85], due to its replication complexity. The aforementioned attacks can be implemented under two different scenarios against the generative models, either black-box or white-box setting [158].…”

Section: Privacy Preservationmentioning

confidence: 99%

A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

Ghosheh¹,

Li²,

Zhu³

2022

Preprint

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Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to review the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning.

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Healthcare Cramér Generative Adversarial Network (HCGAN)

Cited by 11 publications

References 22 publications

DLGNet: Adversarial Reinforcement Learning for Disease Label Generation

DLGNet: Adversarial Reinforcement Learning for Disease Label Generation

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

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