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BioMedInformatics
Special Issues
Deep Learning in Biomedical Informatics: Current Updates and Perspectives
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Deep Learning in Biomedical Informatics: Current Updates and Perspectives

A special issue of BioMedInformatics (ISSN 2673-7426).

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 2934

Special Issue Editor

Dr. Sitthichok Chaichulee

E-Mail Website
Guest Editor
Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hatyai Campus, Songkhla 90110, Thailand.
Interests: machine learning; biomedical signal processing; medical image analysis; natural language processing; healthcare interoperability

Special Issue Information

Dear Colleagues,

Deep learning has been one of the great new driving forces in biomedical informatics. Deep learning is concerned with techniques that use multiple processing layers to learn and make appropriate decisions. Deep learning can be applied in the fields of biomedical signal processing, medical image analysis, clinical natural language processing, electronic health records, bioinformatics, etc. Given the amount of complex biomedical data is rapidly growing, it is critical that these data are transformed into useful and valuable knowledge. Currently, deep learning is being rapidly investigated and implemented in both biomedical research and clinical practice, although the complexity of biomedical data presents numerous challenges. Despite the great progress that has been made in recent years, the potential of deep learning for biomedical informatics has not yet been fully exploited, and further advances are expected to come.

This Special Issue aims to present recent updates and perspectives on deep learning in biomedical informatics. It, therefore, provides an opportunity to share the results of recent developments and applications in all areas of deep learning in biomedical informatics. Comprehensive review articles addressing the topics are also welcome for submission.

Dr. Sitthichok Chaichulee
Guest Editor

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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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

  • Supervised and unsupervised deep learning
  • Biomedical signal processing
  • Medical image analysis
  • Natural language processing
  • Clinical decision support systems
  • Explainable artificial intelligence

Published Papers (1 paper)

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Research

19 pages, 6014 KiB  
Article
Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages
by Julio José Prado and Ignacio Rojas
BioMedInformatics 2021, 1(3), 182-200; https://doi.org/10.3390/biomedinformatics1030012 - 13 Dec 2021
Cited by 2 | Viewed by 2556
Abstract
According to the WHO, approximately 50 million people worldwide have dementia and there are nearly 10 million new cases every year. Alzheimer’s disease is the most common form of dementia and may contribute to 60–70% of cases. It has been proved that early [...] Read more.
According to the WHO, approximately 50 million people worldwide have dementia and there are nearly 10 million new cases every year. Alzheimer’s disease is the most common form of dementia and may contribute to 60–70% of cases. It has been proved that early diagnosis is key to promoting early and optimal management. However, the early stage of dementia is often overlooked and patients are typically diagnosed when the disease progresses to a more advanced stage. The objective of this contribution is to predict Alzheimer’s early stages, not only dementia itself. To carry out this objective, different types of SVM and CNN machine learning classifiers will be used, as well as two different feature selection algorithms: PCA and mRMR. The different experiments and their performance are compared when classifying patients from MRI images. The newness of the experiments conducted in this research includes the wide range of stages that we aim to predict, the processing of all the available information simultaneously and the Segmentation routine implemented in SPM12 for preprocessing. We will make use of multiple slices and consider different parts of the brain to give a more accurate response. Overall, excellent results have been obtained, reaching a maximum F1 score of 0.9979 from the SVM and PCA classifier. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Informatics: Current Updates and Perspectives)
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