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Mathematics
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Data Mining, Machine Learning and Network Analysis in Biomedical Informatics
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Data Mining, Machine Learning and Network Analysis in Biomedical Informatics

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3290

Special Issue Editors

Prof. Dr. Andras Hajdu

E-Mail Website
Guest Editor
Department of Computer Graphics and Image Processing, Faculty of Informatics, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
Interests: digital image processing; bioinformatics; discrete geometry; big data
Dr. Balazs Harangi

E-Mail Website
Guest Editor
Faculty of Informatics, University of Debrecen, Egyetem tér 1, 4032 Debrecen, Hungary
Interests: data science; machine learning

Special Issue Information

Dear Colleagues,

Exploiting data assets with advanced techniques, especially artificial intelligence, is expected to dominate all disciplines, including biomedicine. In addition to their potential to be further developed with appropriate theoretical tools, machine learning-based models can also improve the accuracy of theoretical models by further adopting observed data. This Special Issue aims to present new advances in biomedical informatics, particularly for the better exploitation of biological and clinical data resources for the more efficient operation and development of biomedical informatics systems to improve healthcare solutions.

This Special Issue focuses on, but is not limited to, optimizing or replacing theoretical models with machine learning ones regarding any component of a biomedical information system. In addition to deeper theoretical approaches, we intend to publish contributions that achieve new results with state-of-the-art artificial intelligence-based methods or more traditional data mining or machine learning ones. Within the biomedical field the scope is not limited.

Prof. Dr. Andras Hajdu
Dr. Balazs Harangi
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. Mathematics 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 2600 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
  • automated screening systems
  • big data analysis
  • clinical decision support
  • computational systems
  • data mining
  • databases and datasets
  • deep learning
  • health informatics
  • imaging, image, and signal processing
  • machine learning
  • modeling and simulation
  • natural language processing
  • network analysis
  • optimization
  • sensors and wearable systems

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Published Papers (2 papers)

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Research

15 pages, 527 KiB  
Article
Predicting Stroke Risk Based on ICD Codes Using Graph-Based Convolutional Neural Networks
by Attila Tiba, Tamás Bérczes, Attila Bérczes and Judit Zsuga
Mathematics 2024, 12(12), 1814; https://doi.org/10.3390/math12121814 - 11 Jun 2024
Viewed by 1491
Abstract
In recent years, convolutional neural networks (CNNs) have emerged as highly efficient architectures for image and audio classification tasks, gaining widespread adoption in state-of-the-art methodologies. While CNNs excel in machine learning scenarios where the data representation exhibits a grid structure, they face challenges [...] Read more.
In recent years, convolutional neural networks (CNNs) have emerged as highly efficient architectures for image and audio classification tasks, gaining widespread adoption in state-of-the-art methodologies. While CNNs excel in machine learning scenarios where the data representation exhibits a grid structure, they face challenges in generalizing to other data types. For instance, they struggle with data structured on 3D meshes (e.g., measurements from a network of meteorological stations) or data represented by graph structures (e.g., molecular graphs). To address such challenges, the scientific literature proposes novel graph-based convolutional network architectures, extending the classical convolution concept to data structures defined by graphs. In this paper, we use such a deep learning architecture to examine graphs defined using the ICD-10 codes appearing in the medical data of patients who suffered hemorrhagic stroke in Hungary in the period 2006–2012. The purpose of the analysis is to predict the risk of stroke by examining a patient’s ICD graph. Finally, we also compare the effectiveness of this method with classical machine learning classification methods. The results demonstrate that the graph-based method can predict the risk of stroke with an accuracy of over 73%, which is more than 10% higher than the classical methods. Full article
(This article belongs to the Special Issue Data Mining, Machine Learning and Network Analysis in Biomedical Informatics)
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17 pages, 1131 KiB  
Article
Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles
by János Tóth, Henrietta Tomán, Gabriella Hajdu and András Hajdu
Mathematics 2023, 11(18), 3992; https://doi.org/10.3390/math11183992 - 20 Sep 2023
Cited by 1 | Viewed by 1209
Abstract
An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing [...] Read more.
An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F1 score of 0.9397 and an associated MCC of 0.7757. Our proposed method maintained the solution quality with an F1 score of 0.9395 and MCC of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold. Full article
(This article belongs to the Special Issue Data Mining, Machine Learning and Network Analysis in Biomedical Informatics)
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