Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023

Zhi-Jin Zhang; Hao-Feng Wang; Yu-Sha Cui; Shi-Nuan Lin; Han Jiao; Fan-Gang Meng; Tao Feng

doi:10.1016/j.mmr.2026.100055

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Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023

RESEARCH | Updated：2026-08-21

- Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023
- Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023
- 军事医学研究（英文） 2026年
- Affiliations：
  
  Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
  China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
  National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing 100089, China
  HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
  Guangdong Provincial Engineering Technology Research Center for Medical Artificial Intelligence in Neurological Diseases, Shenzhen 518000, Guangdong, China
  Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Author bio：
  
  Fan-Gang Meng, fgmeng@ccmu.edu.cn
  *Tao Feng, bxbkyjs@sina.com;
- Funds：
  
  the National Natural Science Foundation of China(82271459;82571426);the Beijing High-level Innovative and Entrepreneurial Talent Support Program(G202512038)
- DOI：10.1016/j.mmr.2026.100055
  中图分类号：
- 收稿：2025-10-09，
  
  录用：2026-07-16，
  
  纸质出版：2026-07
- Accepted：
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Zhi-Jin Zhang, Hao-Feng Wang, Yu-Sha Cui, 等. Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023[J/OL]. 军事医学研究（英文）, 2026.

Zhang ZJ, Wang HF, Cui YS, Lin SN, Jiao H, Meng FG, et al. Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023. Mil Med Res. 2026;13(1):100055.
Zhi-Jin Zhang, Hao-Feng Wang, Yu-Sha Cui, 等. Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023[J/OL]. 军事医学研究（英文）, 2026. DOI： 10.1016/j.mmr.2026.100055.

Zhang ZJ, Wang HF, Cui YS, Lin SN, Jiao H, Meng FG, et al. Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023. Mil Med Res. 2026;13(1):100055. DOI： 10.1016/j.mmr.2026.100055.

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Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023

Forecasting the prevalence of epilepsy in low- and middle-income countries to 2050 using a hybrid deep neural network-transformer modeling framework: insights from the Global Burden of Disease Study 2023

DOI：10.1016/j.mmr.2026.100055