TY - GEN
T1 - A Hypertension Early Warning Model Combining Generative Adversarial Networks and Long Short-Term Memory Neural Networks
AU - Lin, Shaofu
AU - Qiao, Ziqian
AU - Chen, Jianhui
AU - Huang, Zhisheng
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Hypertension is one of the most common chronic diseases threatening human health, and early warning and intervention are crucial for controlling disease progression. However, in the real world, the sample data used for hypertension model training is often limited, posing challenges to model performance. To address this issue, this paper proposes an end-to-end hypertension early warning model based on Generative Adversarial Networks (GANs) and Long Short-Term Memory (LSTM) networks, which can generate a large number of high-quality synthetic electronic health records(EHRs) in a small sample environment and directly use them for training the hypertension early warning model. Specifically, we use the processed data from the public MIMIC-III dataset as input and generate a large number of synthetic EHRs through GAN and LSTM networks. The GAN network generates realistic synthetic data through adversarial training of the discriminator and generator, while the LSTM network is used to capture time series features, thereby enhancing the authenticity and diversity of the data. After generating the synthetic data, these data are directly used to train the hypertension early warning model. The feedback mechanism during the generation of EHRs can continuously obtain higher prediction accuracy, thus optimizing the quality of the generated data until the optimal effect is achieved. Finally, the optimal hypertension early warning model and the corresponding synthetic data are saved. Experimental results show that the hypertension early warning model trained using synthetic data significantly improves the prediction accuracy on the test set, with higher sensitivity and specificity compared to traditional methods. This study verifies the effectiveness and superiority of the proposed method in a small sample environment, providing a new perspective for the utilization of large-scale medical data, and has a wide range of application prospects.
AB - Hypertension is one of the most common chronic diseases threatening human health, and early warning and intervention are crucial for controlling disease progression. However, in the real world, the sample data used for hypertension model training is often limited, posing challenges to model performance. To address this issue, this paper proposes an end-to-end hypertension early warning model based on Generative Adversarial Networks (GANs) and Long Short-Term Memory (LSTM) networks, which can generate a large number of high-quality synthetic electronic health records(EHRs) in a small sample environment and directly use them for training the hypertension early warning model. Specifically, we use the processed data from the public MIMIC-III dataset as input and generate a large number of synthetic EHRs through GAN and LSTM networks. The GAN network generates realistic synthetic data through adversarial training of the discriminator and generator, while the LSTM network is used to capture time series features, thereby enhancing the authenticity and diversity of the data. After generating the synthetic data, these data are directly used to train the hypertension early warning model. The feedback mechanism during the generation of EHRs can continuously obtain higher prediction accuracy, thus optimizing the quality of the generated data until the optimal effect is achieved. Finally, the optimal hypertension early warning model and the corresponding synthetic data are saved. Experimental results show that the hypertension early warning model trained using synthetic data significantly improves the prediction accuracy on the test set, with higher sensitivity and specificity compared to traditional methods. This study verifies the effectiveness and superiority of the proposed method in a small sample environment, providing a new perspective for the utilization of large-scale medical data, and has a wide range of application prospects.
KW - Electronic health records
KW - Generative adversarial networks
KW - Hypertension early warning
KW - Long short-term memory networks
KW - MIMIC-III dataset
UR - https://www.scopus.com/pages/publications/105000318427
UR - https://www.scopus.com/inward/citedby.url?scp=105000318427&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-1483-7_29
DO - 10.1007/978-981-96-1483-7_29
M3 - Conference contribution
AN - SCOPUS:105000318427
SN - 9789819614820
T3 - Lecture Notes in Computer Science
SP - 341
EP - 356
BT - Web Information Systems Engineering – WISE 2024 PhD Symposium, Demos and Workshops
A2 - Barhamgi, Mahmoud
A2 - Wang, Hua
A2 - Wang, Xin
A2 - Aïmeur, Esma
A2 - Mrissa, Michael
A2 - Chikhaoui, Belkacem
A2 - Boukadi, Khouloud
A2 - Grati, Rima
A2 - Maamar, Zakaria
PB - Springer Nature
T2 - PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st International Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th International Conference on Web Information Systems Engineering, WISE 2024
Y2 - 2 December 2024 through 5 December 2024
ER -