Artificial Intelligence Algorithms for Medicine (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 15 December 2024 | Viewed by 836

Special Issue Editors


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Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
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Special Issue Information

Dear Colleagues,

In recent decades, the Big Data phenomenon has driven the application of informatics in medicine to solve multiple problems in the field. In particular, the use of artificial intelligence algorithms, specifically machine learning algorithms, is turning out to be very useful in problems of disease prediction, the search for patterns of characteristics to identify populations at risk, the discovery of factors that influence the appearance of diseases, medical image processing and information extraction, and the classification of medical information. In this sense, a field of work has been developed that specializes in the design and application of algorithms specifically aimed at solving problems in medicine. The objective of this Special Issue is to bring together works that show the latest advances in the application of artificial intelligence algorithms in the medical field, as well as specific experiences and applications to specific problems.

The objective of this Special Issue is to serve as a meeting point for all researchers who are working in these fields both theoretically and with an applied focus. The topics of interest include, but are not limited to the following:

  • Machine learning applied to medicine;
  • Artificial intelligence applied to medicine;
  • Big Data and health;
  • The application of artificial intelligence to information processing;
  • Data analysis applied to medicine;
  • Algorithms for medicine;
  • The massive data of medical processing;
  • Medical image processing;
  • e-Health.

Both review articles on the state of the art and experimental or theoretical articles are welcome.

Dr. Antonio Sarasa-Cabezuelo
Dr. Francesc Pozo
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. Algorithms 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 1600 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

  • deep learning
  • machine learning
  • artificial intelligence
  • data analysis
  • algorithms
  • big data

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Published Papers (1 paper)

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Research

22 pages, 3297 KiB  
Article
Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques
by Moises Ramos-Martinez, Felipe D. J. Sorcia-Vázquez, Gerardo Ortiz-Torres, Mario Martínez García, Mayra G. Mena-Enriquez, Estela Sarmiento-Bustos, Juan Carlos Mixteco-Sánchez, Erasmo Misael Rentería-Vargas, Jesús E. Valdez-Resendiz and Jesse Yoe Rumbo-Morales
Algorithms 2024, 17(11), 527; https://doi.org/10.3390/a17110527 - 15 Nov 2024
Viewed by 501
Abstract
Sleep apnea is a sleep disorder that disrupts breathing during sleep. This study aims to classify sleep apnea using a machine learning approach and a Euler–Poincaré characteristic (EPC) model derived from electrocardiogram (ECG) signals. An ensemble K-nearest neighbors classifier and a feedforward neural [...] Read more.
Sleep apnea is a sleep disorder that disrupts breathing during sleep. This study aims to classify sleep apnea using a machine learning approach and a Euler–Poincaré characteristic (EPC) model derived from electrocardiogram (ECG) signals. An ensemble K-nearest neighbors classifier and a feedforward neural network were implemented using the EPC model as inputs. ECG signals were preprocessed with a polynomial-based scheme to reduce noise, and the processed signals were transformed into a non-Gaussian physiological random field (NGPRF) for EPC model extraction from excursion sets. The classifiers were then applied to the EPC model inputs. Using the Apnea-ECG dataset, the proposed method achieved an accuracy of 98.5%, sensitivity of 94.5%, and specificity of 100%. Combining machine learning methods and geometrical features can effectively diagnose sleep apnea from single-lead ECG signals. The EPC model enhances clinical decision-making for evaluating this disease. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine (2nd Edition))
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