A Synthetic Data Generation Model for Diabetic Foot Treatment

Hyun, Jayun; Lee, Seo Hu; Son, Ha Min; Park, Ji Ung; Chung, Tai Myoung

doi:10.1007/978-981-33-4370-2_18

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…This method demonstrated to robustness to manage missing data and shifting trends. The experiment demonstrated that Prophet can express medical changes with effective prediction values based on a specific parameter [18].…”

Section: Content Modelsmentioning

confidence: 99%

“…Dahmen and Cook developed a synthetic smart home senser called Synsys by way of using Hidden Markov models that can generate temporary sequences of daily activities [17]. Prophet is another method developed by Hyun et al that was validated for predicting time-series data using an additive model with a non-linear trend fit [18]. This method demonstrated to robustness to manage missing data and shifting trends.…”

Section: Content Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Synthetic data & the future of Women’s Health: A synergistic relationship

Delanerolle,

Phiri,

Cavalini

et al. 2023

International Journal of Medical Informatics

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Section: Content Modelsmentioning

confidence: 99%

Section: Content Modelsmentioning

confidence: 99%

Synthetic data & the future of Women’s Health: A synergistic relationship

Delanerolle,

Phiri,

Cavalini

et al. 2023

International Journal of Medical Informatics

View full text Add to dashboard Cite

“…Prophet is a procedure used by Hyun et al [59] for forecasting time-series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It is robust to missing data and shifting trends, and typically handles outliers well.…”

Section: Prophetmentioning

confidence: 99%

Synthetic data generation for tabular health records: A systematic review

et al. 2022

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Synthetic Data Generation System for AI-Based Diabetic Foot Diagnosis

et al. 2021

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The paucity of readily available medical data poses a major challenge for the development of AI (artificial intelligence)-based healthcare applications and devices. To aid in overcoming this challenge, we propose a sensor-based medical time series data synthesis system especially designed for the training of diabetic foot diagnosis models. The proposed system utilizes statistical methods, augmentation techniques, and the NeuralProphet model to accomplish its purpose while still maintaining medical validity. Our results show that the generated synthetic time series data follow the trends and tendencies of real data. We also verify our work using machine learning-based clustering. By successfully clustering the synthetic data generated by our proposed system, we prove that our system is capable of meeting its objectives.

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A Synthetic Data Generation Model for Diabetic Foot Treatment

Cited by 6 publications

References 20 publications

Synthetic data & the future of Women’s Health: A synergistic relationship

Synthetic data & the future of Women’s Health: A synergistic relationship

Synthetic data generation for tabular health records: A systematic review

Synthetic Data Generation System for AI-Based Diabetic Foot Diagnosis

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