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ChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis

ChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis

The mean squared error (MSE) was used as the loss function for training all deep learning models in this study, and it is defined as MSE=1n∑i=1n(yi−y^i)2 , where y^i represents the predictive value and yi represents the actual value, and n is the total number of observations. The model’s parameters were fine-tuned using gradient descent learning, particularly using the Adam optimizer alongside backpropagation.

Weihong Huang, Wudi Wei, Xiaotao He, Baili Zhan, Xiaoting Xie, Meng Zhang, Shiyi Lai, Zongxiang Yuan, Jingzhen Lai, Rongfeng Chen, Junjun Jiang, Li Ye, Hao Liang

J Med Internet Res 2025;27:e74423

Examining the Impact of Digital Inclusion on Depression Among Older Adults in China: Mediating Role of Noncognitive Abilities

Examining the Impact of Digital Inclusion on Depression Among Older Adults in China: Mediating Role of Noncognitive Abilities

For example, Wang et al [12] found that 97.15% of Chinese older adults experience digital exclusion, which strongly correlates with cognitive impairment. However, cognitive abilities show relative stability in early life [13], although they can adapt to individual experiences in middle and later life [14]. In contrast, noncognitive abilities exhibit greater plasticity over time [15]. This has shifted research attention to noncognitive abilities, such as personality traits.

Xinru Li, Chengyu Chen, Xiyan Li, Yuyang Li, Shujuan Xiao, Jianan Han, Yanan Wang, Chichen Zhang

J Med Internet Res 2025;27:e71441