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 452

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Department of Clinical and Experimental Medicine, Università degli Studi di Foggia, 71122 Foggia, FG, Italy
Interests: bioinformatics; artificial intelligence
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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

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Keywords

  • artificial intelligence models
  • biomedicine
  • machine learning methods
  • artificial neural networks
  • precision medicine
  • personalized health care

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Published Papers (1 paper)

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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
Viewed by 198
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
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