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Applied Sciences
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Application of Biomedical Informatics
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Application of Biomedical Informatics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 3607

Special Issue Editors

Dr. Yung-Fu Chen

E-Mail Website
Guest Editor
Department Dental Technology and Material Sciences, Central Taiwan University of Science and Technology, Taichung 406053, Taiwan
Interests: biomedical signal processing; biomedical image processing; decision support system; biomedical engineering
Prof. Dr. Yung-Kuan Chan

E-Mail Website
Guest Editor
Department of Management Information Systems, National Chung Hsing University, Taichung 40227, Taiwan
Interests: image processing; medical imaging technology; data exploration; management mathematics; database management systems;, algorithms; advanced image processing; STEM education
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Eldessouki

E-Mail Website
Guest Editor
Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, 46001 Liberec, Czech Republic
Interests: artificial intelligence; image analysis; soft-computing; biomaterials; biosensors; mathematical modeling; computer simulations

Special Issue Information

Dear Colleagues,

Biomedical informatics is an interdisciplinary field that involves studying and pursuing effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health. The purpose of this Special Issue is to demonstrate the development and potential of important applications and technologies of biomedical informatics. The main focus of this Special Issue will be on the proposal of applications or techniques of biomedical informatics that may be associated with artificial intelligence, biomedical signal and image processing, medical device, medical system, biomedical translation, health big data, biomedical privacy and security, genetics, public health, health information management, preventive medicine, or precision medicine. This Special Issue aims to serve as an international platform of important references for researchers to survey and summarize the most recent developments in biomedical informatics. Potential topics include but are not limited to:

  • Artificial Intelligence in Biomedicine and Health Care
  • Preventive Medicine
  • Health Big Data
  • Biomedical Privacy and Security
  • Medical Devices and Systems
  • Public Health and Health Information Management
  • Techniques and Systems of Biomedical Translation, Genetics, and Precision Medicine
  • Biomedical Sensing as well as Biomedical Signal and Image Processing
  • Biomedical Expert Systems and Clinical Decision Support Systems

Dr. Yung-Fu Chen
Dr. Yung-Kuan Chan
Dr. Mohamed Eldessouki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • biomedical signal processing
  • biomedical image processing
  • medical device
  • medical system
  • biomedical translation
  • health big data
  • biomedical privacy
  • biomedical security
  • genetics
  • public health
  • health information management
  • preventive medicine
  • precision medicine

Published Papers (2 papers)

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Research

19 pages, 4374 KiB  
Article
Guidance for Clinical Evaluation under the Medical Device Regulation through Automated Scoping Searches
by Fu-Sung Kim-Benjamin Tang, Mark Bukowski, Thomas Schmitz-Rode and Robert Farkas
Appl. Sci. 2023, 13(13), 7639; https://doi.org/10.3390/app13137639 - 28 Jun 2023
Viewed by 1045
Abstract
The Medical Device Regulation (MDR) in Europe aims to improve patient safety by increasing requirements, particularly for the clinical evaluation of medical devices. Before the clinical evaluation is initiated, a first literature review of existing clinical knowledge is necessary to decide how to [...] Read more.
The Medical Device Regulation (MDR) in Europe aims to improve patient safety by increasing requirements, particularly for the clinical evaluation of medical devices. Before the clinical evaluation is initiated, a first literature review of existing clinical knowledge is necessary to decide how to proceed. However, small and medium-sized enterprises (SMEs) lacking the required expertise and funds may disappear from the market. Automating searches for the first literature review is both possible and necessary to accelerate the process and reduce the required resources. As a contribution to the prevention of the disappearance of SMEs and respective medical devices, we developed and tested two automated search methods with two SMEs, leveraging Medical Subject Headings (MeSH) terms and Bidirectional Encoder Representations from Transformers (BERT). Both methods were tailored to the SMEs and evaluated through a newly developed workflow that incorporated feedback resource-efficiently. Via a second evaluation with the established CLEF 2018 eHealth TAR dataset, the more general suitability of the search methods for retrieving relevant data was tested. In the real-world use case setting, the BERT-based method performed better with an average precision of 73.3%, while in the CLEF 2018 eHealth TAR evaluation, the MeSH-based search method performed better with a recall of 86.4%. Results indicate the potential of automated searches to provide device-specific relevant data from multiple databases while screening fewer documents than in manual literature searches. Full article
(This article belongs to the Special Issue Application of Biomedical Informatics)
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19 pages, 2257 KiB  
Article
Analyzing Brain Waves of Table Tennis Players with Machine Learning for Stress Classification
by Yu-Hung Tsai, Sheng-Kuang Wu, Shyr-Shen Yu and Meng-Hsiun Tsai
Appl. Sci. 2022, 12(16), 8052; https://doi.org/10.3390/app12168052 - 11 Aug 2022
Cited by 5 | Viewed by 1663
Abstract
Electroencephalography (EEG) has been widely used in the research of stress detection in recent years; yet, how to analyze an EEG is an important issue for upgrading the accuracy of stress detection. This study aims to collect the EEG of table tennis players [...] Read more.
Electroencephalography (EEG) has been widely used in the research of stress detection in recent years; yet, how to analyze an EEG is an important issue for upgrading the accuracy of stress detection. This study aims to collect the EEG of table tennis players by a stress test and analyze it with machine learning to identify the models with optimal accuracy. The research methods are collecting the EEG of table tennis players using the Stroop color and word test and mental arithmetic, extracting features by data preprocessing and then making comparisons using the algorithms of logistic regression, support vector machine, decision tree C4.5, classification and regression tree, random forest, and extreme gradient boosting (XGBoost). The research findings indicated that, in three-level stress classification, XGBoost had an 86.49% accuracy in the case of the generalized model. This study outperformed other studies by up to 11.27% in three-level classification. The conclusion of this study is that a stress detection model that was built with the data on the brain waves of table tennis players could distinguish high stress, medium stress, and low stress, as this study provided the best classifying results based on the past research in three-level stress classification with an EEG. Full article
(This article belongs to the Special Issue Application of Biomedical Informatics)
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