Journal of Medical Internet Research

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Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study

Shao-Chi Lu, Guang-Yuan Chen, An-Sheng Liu, Jen-Tang Sun, Jun-Wan Gao, Chien-Hua Huang, Chu-Lin Tsai, Li-Chen Fu

J Med Internet Res 2025;27:e67576

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Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study

Yu-Ting Lin, Yuan-Xiang Deng, Chu-Lin Tsai, Chien-Hua Huang, Li-Chen Fu

JMIR Med Inform 2024;12:e48862

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A Digital Screening System for Alzheimer Disease Based on a Neuropsychological Test and a Convolutional Neural Network: System Development and Validation

Li et al [15] used fusion information from MRI and PET scans for feature selection, processed the selected features through restricted Boltzmann machines to obtain the learned features, and applied the learned features to an SVM model for the classification of the different stages of AD. However, neuroimaging is not cheap. Moreover, patients who experience claustrophobia cannot undergo scanning by the machine because patients need to lie motionless inside the closed shell of the machine.

Wen-Ting Cheah, Jwu-Jia Hwang, Sheng-Yi Hong, Li-Chen Fu, Yu-Ling Chang, Ta-Fu Chen, I-An Chen, Chun-Chen Chou