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Topic Editors

Dr. Dawei Yang
Zhongshan Hospital Fudan University, Shanghai Engineer & Technology Research Center of Internet of Things for Respiratory Medicine, Shanghai, China
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
Dr. Hongyi Xin
UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai, China
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Research on Data Mining of Electronic Health Records Using Deep Learning Methods

Abstract submission deadline
30 June 2025
Manuscript submission deadline
31 August 2025
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2360

Topic Information

Dear Colleagues,

With the explosive expansion of medical information, the era of Health 4.0 has arrived, encompassing a flood of medical data, such as electronic health records, medical images, continuous digital recordings of vital signs, etc. The integration of artificial intelligence into this field is thus unavoidable. However, frontline medical healthcare workers have a limited knowledge of coding, deep learning, and artificial intelligence, creating an unseen barrier to efficient application due to fear of replacement. Thus, for this Special Issue, we are inviting well-regarded physicians, informatics technology scientists, computer science engineers, and medical internet of things specialists to share their experience and studies in this field. Explaining the mys-tery of data mining could lead the way to improved healthcare quality.

Dr. Dawei Yang
Dr. Yu Zhu
Dr. Hongyi Xin
Topic Editors

Keywords

  • data mining
  • electronic health records
  • deep learning
  • artificial intelligence
  • medical internet of things
  • metaverse in medicine
  • telemedicine
  • ehealth
  • screening

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms 		1.8 4.1 2008 18.9 Days CHF 1600 Submit
Future Internet
futureinternet 		2.8 7.1 2009 16.9 Days CHF 1600 Submit
Information
information 		2.4 6.9 2010 16.4 Days CHF 1600 Submit
Mathematics
mathematics 		2.3 4.0 2013 18.3 Days CHF 2600 Submit
Symmetry
symmetry 		2.2 5.4 2009 17.3 Days CHF 2400 Submit

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Published Papers (1 paper)

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26 pages, 9869 KiB  
Article
Comparative Feature-Guided Regression Network with a Model-Eye Pretrained Model for Online Refractive Error Screening
by Jiayi Wang, Tianyou Zheng, Yang Zhang, Tianli Zheng and Weiwei Fu
Future Internet 2025, 17(4), 160; https://doi.org/10.3390/fi17040160 - 3 Apr 2025
Viewed by 186
Abstract
With the development of the internet, the incidence of myopia is showing a trend towards younger ages, making routine vision screening increasingly essential. This paper designs an online refractive error screening solution centered on the CFGN (Comparative Feature-Guided Network), a refractive error screening [...] Read more.
With the development of the internet, the incidence of myopia is showing a trend towards younger ages, making routine vision screening increasingly essential. This paper designs an online refractive error screening solution centered on the CFGN (Comparative Feature-Guided Network), a refractive error screening network based on the eccentric photorefraction method. Additionally, a training strategy incorporating an objective model-eye pretraining model is introduced to enhance screening accuracy. Specifically, we obtain six-channel infrared eccentric photorefraction pupil images to enrich image information and design a comparative feature-guided module and a multi-channel information fusion module based on the characteristics of each channel image to enhance network performance. Experimental results show that CFGN achieves an accuracy exceeding 92% within a ±1.00 D refractive error range across datasets from two regions, with mean absolute errors (MAEs) of 0.168 D and 0.108 D, outperforming traditional models and meeting vision screening requirements. The pretrained model helps achieve better performance with small samples. The vision screening scheme proposed in this study is more efficient and accurate than existing networks, and the cost-effectiveness of the pretrained model with transfer learning provides a technical foundation for subsequent rapid online screening and routine tracking via networking. Full article
(This article belongs to the Topic Research on Data Mining of Electronic Health Records Using Deep Learning Methods)
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