Grouped Correlational Generative Adversarial Networks for Discrete Electronic Health Records

Yang, Fan; Yu, Zhongping; Liang, Yunfan; Gan, Xiaolu; Kang, Lin; Zou, Quan; Zeng, Yanjun

doi:10.1109/bibm47256.2019.8983215

Cited by 15 publications

(26 citation statements)

References 11 publications

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“…-Visualized embeding The real and synthetic data samples are plotted on a graph or projected into an embeding such as t-SNE or PCA and compared visually. (Cui et al, 2019;Yu et al, 2019;Zhu et al, 2020a;Yale et al, 2019a;Yang et al, 2019c;Beaulieu-Jones et al, 2019;Tantipongpipat et al, 2019;Dash et al, 2019).…”

Section: Qualitative Evaluationmentioning

confidence: 99%

“…Inter-dimensional correlation Dimension-wise Pearson coefficient correlation matrices for both real and synthetic data (Beaulieu-Jones et al, 2019;Goncalves et al, 2020;Torfi and Beyki, 2019;Frid-Adar et al, 2018;Ozyigit et al, 2020;Yang et al, 2019c;Zhu et al, 2020a;Walsh et al, 2020;Yale et al, 2019a;Ozyigit et al, 2020;Dash et al, 2019;Bae et al, 2020b).…”

Section: Metric Descriptionmentioning

confidence: 99%

“…Defined by Yang et al as a requirement for records to contain both disease and medication instances. (Yang et al, 2019c).…”

Section: Proportion Of Valid Samplesmentioning

confidence: 99%

“…similarity Measure of similarity and diversity defined on sets as the size of the intersection over the size of the union(Ozyigit et al, 2020;Yang et al, 2019c; contributors).…”

mentioning

confidence: 99%

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Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Geogres-Filteau¹,

Cirillo

2020

Preprint

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After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it.Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging.The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above.Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics.We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.

show abstract

Section: Qualitative Evaluationmentioning

confidence: 99%

Section: Metric Descriptionmentioning

confidence: 99%

“…Defined by Yang et al as a requirement for records to contain both disease and medication instances. (Yang et al, 2019c).…”

Section: Proportion Of Valid Samplesmentioning

confidence: 99%

“…similarity Measure of similarity and diversity defined on sets as the size of the intersection over the size of the union(Ozyigit et al, 2020;Yang et al, 2019c; contributors).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Geogres-Filteau¹,

Cirillo

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The prediction performance of the model is compared against benchmarks of the same type on real data (Cui et al, 2019;Yoon et al, 2018a;Yu et al, 2019;Zhu et al, 2020a;Baowaly et al, 2019;Wang et al, 2019a;Walsh et al, 2020;Yoon et al, 2018b;McDermott et al, 2018;Yang et al, 2019c;Yoon et al, 2018c;Beaulieu-Jones et al, 2019;Bae et al, 2020a). Models trained to make forward predictions from past observations or from real data transformed with a known function can simply be evaluated for accuracy.…”

Section: Accuracymentioning

confidence: 99%

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Geogres-Filteau¹,

Cirillo

2020

Preprint

View full text Add to dashboard Cite

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.

show abstract

Synthetic data generation: State of the art in health care domain

Murtaza

Ahmed

Khan

et al. 2023

Computer Science Review

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Grouped Correlational Generative Adversarial Networks for Discrete Electronic Health Records

Cited by 15 publications

References 11 publications

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

Synthetic data generation: State of the art in health care domain

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