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Bioengineering
Special Issues
Biomedical Application of Big Data and Artificial Intelligence—Second Edition
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Biomedical Application of Big Data and Artificial Intelligence—Second Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2006

Special Issue Editors

Dr. Yan Pei

E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
Interests: computational intelligence; machine learning; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Jijiang Yang

E-Mail Website
Guest Editor
Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
Interests: big data analysis; medical image processing; complex system design and integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Big data is pervasive and innately interdisciplinary, and its primary research subject does not only relate to the amount of data but also to how to develop effective and efficient analytics methods and algorithms for knowledge discovery. Artificial intelligence is one of the new methodologies to advance data science, data mining, and medical and health informatics using the theory and methodology of big data. Big data and artificial intelligence provide more research opportunities in biomedical practices and applications, which benefits research, development, and industrial applications of big data and artificial intelligence.

This Special Issue provides a framework to discuss and study biomedical applications from the perspectives of big data and artificial intelligence. We invite researchers to contribute to this issue by submitting comprehensive reviews, case studies, and research articles in the field of theoretical and methodological interdisciplinary big data and artificial intelligence for biomedical applications. In particular, artificial intelligence and big data technologies specifically devised, adapted, or tailored to address problems in biomedical applications or biomedical applications that were demonstrated to be particularly effective at being solved by artificial intelligence and big data technologies are welcome.

Dr. Yan Pei
Dr. Jijiang Yang
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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
  • big data
  • image processing
  • data mining
  • soft computing
  • bioinformatics
  • bioengineering
  • healthcare

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Published Papers (2 papers)

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Research

19 pages, 500 KiB  
Article
Comparative Analysis of Large Language Models in Chinese Medical Named Entity Recognition
by Zhichao Zhu, Qing Zhao, Jianjiang Li, Yanhu Ge, Xingjian Ding, Tao Gu, Jingchen Zou, Sirui Lv, Sheng Wang and Ji-Jiang Yang
Bioengineering 2024, 11(10), 982; https://doi.org/10.3390/bioengineering11100982 - 29 Sep 2024
Viewed by 927
Abstract
The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the [...] Read more.
The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the effectiveness of LLMs on biomedical named entity recognition (BNER) tasks remains relatively limited, especially in the context of Chinese text. In this study, we extensively evaluate several typical LLMs, including ChatGLM2-6B, GLM-130B, GPT-3.5, and GPT-4, on the Chinese BNER task by leveraging a real-world Chinese electronic medical record (EMR) dataset and a public dataset. The experimental results demonstrate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for Chinese BNER tasks. More importantly, instruction fine-tuning significantly enhances the performance of LLMs. The fine-tuned offline ChatGLM2-6B surpassed the performance of the task-specific model BiLSTM+CRF (BC) on the real-world dataset. The best fine-tuned model, GPT-3.5, outperforms all other LLMs on the publicly available CCKS2017 dataset, even surpassing half of the baselines; however, it still remains challenging for it to surpass the state-of-the-art task-specific models, i.e., Dictionary-guided Attention Network (DGAN). To our knowledge, this study is the first attempt to evaluate the performance of LLMs on Chinese BNER tasks, which emphasizes the prospective and transformative implications of utilizing LLMs on Chinese BNER tasks. Furthermore, we summarize our findings into a set of actionable guidelines for future researchers on how to effectively leverage LLMs to become experts in specific tasks. Full article
(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence—Second Edition)
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17 pages, 1871 KiB  
Article
Application of Machine Learning in the Diagnosis of Early Gastric Cancer Using the Kyoto Classification Score and Clinical Features Collected from Medical Consultations
by Xue Sun, Liping Zhang, Qingfeng Luo, Yan Zhou, Jun Du, Dongmei Fu, Ziyu Wang, Yi Lei, Qing Wang and Li Zhao
Bioengineering 2024, 11(10), 973; https://doi.org/10.3390/bioengineering11100973 - 27 Sep 2024
Viewed by 803
Abstract
The early detection accuracy of early gastric cancer (EGC) determines the choice of the optimal treatment strategy and the related medical expenses. We aimed to develop a simple, affordable, and time-saving diagnostic model using six machine learning (ML) algorithms for the diagnosis of [...] Read more.
The early detection accuracy of early gastric cancer (EGC) determines the choice of the optimal treatment strategy and the related medical expenses. We aimed to develop a simple, affordable, and time-saving diagnostic model using six machine learning (ML) algorithms for the diagnosis of EGC. It is based on the endoscopy-based Kyoto classification score obtained after the completion of endoscopy and other clinical features obtained after medical consultation. We retrospectively evaluated 1999 patients who underwent gastrointestinal endoscopy at the China Beijing Hospital. Of these, 203 subjects were diagnosed with EGC. The data were randomly divided into training and test sets (ratio 4:1). We constructed six ML models, and the developed models were evaluated on the testing set. This procedure was repeated five times. The Kolmogorov–Arnold Networks (KANs) model achieved the best performance (mean AUC value: 0.76; mean balanced accuracy: 70.96%; mean precision: 58.91%; mean recall: 70.96%; mean false positive rate: 26.11%; mean false negative rate: 31.96%; and mean F1 score value: 58.46). The endoscopy-based Kyoto classification score was the most important feature with the highest feature importance score. The results suggest that the KAN model, the optimal ML model in this study, has the potential to identify EGC patients, which may result in a reduction in both the time cost and medical expenses in clinical practice. Full article
(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence—Second Edition)
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