Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
3.8
Journals
Bioengineering
Special Issues
Machine Learning and Artificial Intelligence for Biomedical Applications, 3rd Edition
share announcement

Machine Learning and Artificial Intelligence for Biomedical Applications, 3rd Edition

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1330

Special Issue Editor

Dr. Crescenzio Gallo

E-Mail Website
Guest Editor
Department of Clinical and Experimental Medicine, Università degli Studi di Foggia, 71122 Foggia, FG, Italy
Interests: bioinformatics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, an increase in the accuracy of information technology has led to several scientific breakthroughs. The first researchers to benefit from improved hardware components have been the developers of artificial intelligence algorithms, who have been able to apply these algorithms in several scientific fields, including biomedicine. Biomedicine is a field of medicine that applies the principles of biology and natural sciences to the development of relevant technologies for healthcare. The combination of artificial intelligence algorithms and biomedicine has led to many applications, such as image analysis of human organs using magnetic resonance images (MRI); DNA/RNA sequencing and protein structure interactions and predictions; and analysis of different biosignals via methods involving electroencephalograms (EEGs), electromyography (EMGs), and electrocardiograms (ECGs).

In this context, machine learning algorithms enable us to learn from observational data and construct highly accurate artificial intelligence models to support the physician. However, obtaining models with high accuracy may not be enough, as AI-based biomedical decisions must be understandable to the physician. Therefore, it is necessary to equip machine learning methods with explainability capacity, leading to explainable artificial intelligence techniques that enable the physician to understand the decisions suggested by the models they use.

This is the third volume of our Special Issue series "Machine Learning and Artificial Intelligence for Biomedical Applications". Please feel free to download and read the first two volumes via the following links:
https://www.mdpi.com/journal/bioengineering/special_issues/39708P1H4A
https://www.mdpi.com/journal/bioengineering/special_issues/483SCWZ885

Dr. Crescenzio Gallo
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. 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 models
  • biomedicine
  • machine learning methods
  • artificial neural networks
  • precision medicine
  • personalized health care

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 1016 KiB  
Article
Graph Representation Learning for the Prediction of Medication Usage in the UK Biobank Based on Pharmacogenetic Variants
by Bill Qi and Yannis J. Trakadis
Bioengineering 2025, 12(6), 595; https://doi.org/10.3390/bioengineering12060595 - 31 May 2025
Viewed by 242
Abstract
Ineffective treatment and side effects are associated with high burdens for the patient and society. We investigated the application of graph representation learning (GRL) for predicting medication usage based on individual genetic data in the United Kingdom Biobank (UKBB). A graph convolutional network [...] Read more.
Ineffective treatment and side effects are associated with high burdens for the patient and society. We investigated the application of graph representation learning (GRL) for predicting medication usage based on individual genetic data in the United Kingdom Biobank (UKBB). A graph convolutional network (GCN) was used to integrate interconnected biomedical entities in the form of a knowledge graph as part of a machine learning (ML) prediction model. Data from The Pharmacogenomics Knowledgebase (PharmGKB) was used to construct a biomedical knowledge graph. Individual genetic data (n = 485,754) from the UKBB was obtained and preprocessed to match with pharmacogenetic variants in the PharmGKB. Self-reported medication usage labels were obtained from UKBB data field 20003. We hypothesize that pharmacogenetic variants can predict the impact of medications on individuals. We assume that an individual using a medication on a regular basis experiences a net benefit (vs. side-effects) from the medication. ML models were trained to predict medication usage for 264 medications. The GCN model significantly outperformed both a baseline logistic regression model (p-value: 1.53 × 10−9) and a deep neural network model (p-value: 8.68 × 10−8). The GCN model also significantly outperformed a GCN model trained using a random graph (GCN-random) (p-value: 5.44 × 10−9). A consistent trend of medications with higher sample sizes having better performance was observed, and for several medications, a high relative rank of the medication (among multiple medications) was associated with greater than 2-fold higher odds of usage of the medication. In conclusion, a graph-based ML approach could be useful in advancing precision medicine by prioritizing medications that a patient may need based on their genetic data. However, further research is needed to improve the quality and quantity of genetic data and to validate our approach using more reliable medication labels. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 3rd Edition)
►▼ Show Figures

Graphical abstract

16 pages, 6739 KiB  
Article
A Multitask Deep Learning Model for Predicting Myocardial Infarction Complications
by Fazliddin Makhmudov, Normakhmad Ravshanov, Dilshot Akhmedov, Oleg Pekos, Dilmurod Turimov and Young-Im Cho
Bioengineering 2025, 12(5), 520; https://doi.org/10.3390/bioengineering12050520 - 14 May 2025
Viewed by 334
Abstract
Myocardial infarction is one of the most severe forms of ischemic heart disease, associated with high mortality and disability worldwide. The accurate and reliable prediction of adverse cardiovascular events is critical for developing effective treatment strategies and improving outcomes in cardiac rehabilitation. Traditional [...] Read more.
Myocardial infarction is one of the most severe forms of ischemic heart disease, associated with high mortality and disability worldwide. The accurate and reliable prediction of adverse cardiovascular events is critical for developing effective treatment strategies and improving outcomes in cardiac rehabilitation. Traditional prognostic models, such as the GRACE and TIMI scores, often lack the flexibility to incorporate a wide range of contemporary clinical predictors. Therefore, machine learning methods, particularly deep neural networks, have recently emerged as promising alternatives capable of enhancing predictive accuracy and enabling more personalized care. This study presents a multitask deep learning model designed to simultaneously address two related tasks: multidimensional binary classification of myocardial infarction complications and multiclass classification of mortality causes. The model was trained on a dataset of 1700 patients, encompassing 111 clinical and demographic features. Experimental results demonstrate high predictive accuracy and the model’s capacity to capture complex interactions among risk factors, suggesting its potential as a valuable tool for clinical decision support in cardiology. Comparative analysis confirms that the proposed multitask approach performs comparably to, or better than, conventional machine learning models. Future research will focus on refining the model and validating its generalizability in real-world clinical environments. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 3rd Edition)
►▼ Show Figures

Figure 1

14 pages, 1138 KiB  
Article
Utility of Clustering in Mortality Risk Stratification in Pulmonary Hypertension
by Pasquale Tondo, Lucia Tricarico, Giuseppe Galgano, Maria Pia C. Varlese, Daphne Aruanno, Crescenzio Gallo, Giulia Scioscia, Natale D. Brunetti, Michele Correale and Donato Lacedonia
Bioengineering 2025, 12(4), 408; https://doi.org/10.3390/bioengineering12040408 - 11 Apr 2025
Cited by 1 | Viewed by 298
Abstract
Background: Pulmonary hypertension (PH) is a condition characterized by increased pressure in the pulmonary arteries with poor prognosis and, therefore, an optimal management is necessary. The study’s aim was to search for PH phenotypes and develop a predictive model of five-year mortality [...] Read more.
Background: Pulmonary hypertension (PH) is a condition characterized by increased pressure in the pulmonary arteries with poor prognosis and, therefore, an optimal management is necessary. The study’s aim was to search for PH phenotypes and develop a predictive model of five-year mortality using machine learning (ML) algorithms. Methods: This multicenter study was conducted on 122 PH patients. Clinical and demographic data were collected and then used to identify phenotypes through clustering. Subsequently, a predictive model was performed by different ML algorithms. Results: Three PH clusters were identified: Cluster 1 (mean age 68.57 ± 10.54) includes 57% females, 69% from non-respiratory PH groups, and better cardiac (NYHA class 2.61 ± 0.84) and respiratory function (FEV1% 78.78 ± 21.54); Cluster 2 includes 50% females, mean age of 71.36 ± 8.32 years, 44% from PH group 3, worse respiratory function (FEV 1% 68.12 ± 10.20); intermediate cardiac function (NYHA class 3.18 ± 0.49) and significantly higher mortality (75%); Cluster 3 represents the youngest cluster (mean age 61.11 ± 13.50) with 65% males, 81% from non-respiratory PH groups, intermediate respiratory function (FEV1% 70.51 ± 17.91) and worse cardiac performance (NYHA class 3.22 ± 0.58). After testing ML models, logistic regression showed the best predictive performance (AUC = 0.835 and accuracy = 0.744) and identified three mortality-risk factors: age, NYHA class, and number of medications taken. Conclusions: The results suggest that the integration of ML into clinical practice can improve risk stratification to optimize treatment strategies and improve outcomes for PH patients. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence for Biomedical Applications, 3rd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop