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Deep Supplement Learning for Healthcare and Biomedical Applications
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Center for Environmental Research and Technology (CE-CERT), University of California Riverside, CA 92521, USA
Prof. Dr. Ömer Faruk Ertuǧrul
Department of Electrical and Electronics Engineering, Batman University, Batman 72100, Turkey
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Deep Supplement Learning for Healthcare and Biomedical Applications

Abstract submission deadline
closed (30 April 2025)
Manuscript submission deadline
30 June 2025
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2641

Topic Information

Dear Colleagues,

During the challenging years of the ongoing COVID-19 pandemic, the need for efficient artificial intelligence models, tools, and applications in healthcare has been more evident than ever. Deep learning algorithms, supplemented by advanced learning techniques, have the potential to revolutionize healthcare and biomedical technologies. This topic aims to bring together interdisciplinary approaches, focusing on innovative applications and existing AI methodologies to address pressing challenges in the healthcare sector. The topics of interest include deep learning algorithms in healthcare, supplemental learning techniques, biomedical data analysis, and medical image processing. These areas are crucial for interpreting genomic data, developing predictive models, and creating health monitoring systems. AI-driven drug discovery, personalized medicine, and the integration of deep learning with electronic health records (EHRs) are also key focal points. Moreover, this topic will explore the development of clinical decision support systems, healthcare diagnostics, and AI-driven diagnosis in healthcare. The role of AI in health science education, rehabilitation, assistive technologies, public health, and ethical and legal considerations in AI healthcare applications will also be addressed. By highlighting the latest research, innovative approaches, and practical implementations, this topic aims to improve patient outcomes, enhance diagnostic accuracy, and optimize treatment protocols. Researchers are encouraged to develop new or adapt existing AI models, tools, and applications to effectively solve the dynamic and heterogeneous nature of healthcare data problems. In addition to the open call for papers, extended versions of articles presented at relevant conferences are invited. Each submission should contain at least 50% new material, such as technical extensions, more in-depth evaluations, or additional use cases, to contribute significantly to the scientific literature in this field.

Prof. Dr. Tahir Cetin Akinci
Prof. Dr. Ömer Faruk Ertuğrul
Topic Editors

Keywords

  • deep learning algorithms in healthcare and supplemental learning techniques
  • medical image processing and biomedical data analysis
  • cognitive systems
  • genomic data interpretation
  • predictive modeling in healthcare
  • health monitoring systems and clinical decision support systems
  • AI-driven drug discovery
  • AI and its applications in medicine
  • integration of deep learning with electronic health records (EHRs)
  • healthcare diagnostics and personalized medicine
  • AI-driven diagnosis in healthcare
  • AI in health science education
  • rehabilitation and assistive technologies
  • AI in public health
  • ethical and legal considerations in AI healthcare applications

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		3.1 7.2 2020 18.9 Days CHF 1600 Submit
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400 Submit
Electronics
electronics 		2.6 5.3 2012 16.4 Days CHF 2400 Submit
Machine Learning and Knowledge Extraction
make 		4.0 6.3 2019 20.8 Days CHF 1800 Submit

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Published Papers (1 paper)

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17 pages, 2856 KiB  
Article
Improved Deep Learning for Parkinson’s Diagnosis Based on Wearable Sensors
by Jintao Yu, Ke Meng, Tingwei Liang, He Liu and Xiaowen Wang
Electronics 2024, 13(23), 4638; https://doi.org/10.3390/electronics13234638 - 25 Nov 2024
Viewed by 1859
Abstract
Parkinson’s disease is a neurodegenerative disease that seriously affects the quality of life of patients. In this study, we propose a new Parkinson’s diagnosis method using deep learning techniques. The method takes multi-channel sensor signals as inputs, and the full convolutional and LSTM [...] Read more.
Parkinson’s disease is a neurodegenerative disease that seriously affects the quality of life of patients. In this study, we propose a new Parkinson’s diagnosis method using deep learning techniques. The method takes multi-channel sensor signals as inputs, and the full convolutional and LSTM blocks of the model perceive the same time-series inputs from two different views, and connect the extracted spatial features with temporal features. In order to improve the detection performance, a channel attention mechanism was incorporated into the model, and a data augmentation approach was used to eliminate the effect of unbalanced datasets on model training. The pd vs. hc and pd vs. dd classification tasks were performed, which improved accuracy by 4.25% and 8.03%, respectively, compared to the previous best results. Both improvements were higher than the previous methods using machine learning combined with feature extraction. To utilize the available data resources more effectively, this study conducted the pd vs. hc vs. dd triple classification task for the first time, which improved the model’s ability to identify disease features. In that task, the accuracy rate reached 78.23%. The experimental results fully demonstrated the effectiveness of the proposed deep learning method for Parkinson’s diagnosis. Full article
(This article belongs to the Topic Deep Supplement Learning for Healthcare and Biomedical Applications)
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