Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
1.9
Journals
Computation
Special Issues
Artificial Intelligence Applications in Public Health: 2nd Edition
share announcement

Artificial Intelligence Applications in Public Health: 2nd Edition

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3867

Special Issue Editors

Dr. Dmytro Chumachenko

E-Mail Website1 Website2
Guest Editor
1. Mathematical Modeling and Artificial Intelligence, National Aerospace University “Kharkiv Aviation Institute”, 61101 Kharkiv, Ukraine
2. Ubiquitous Health Technologies Lab, University of Waterloo, Waterloo, ON N2L 3G5, Canada
3. Balsillie School of International Affairs, Waterloo, ON N2L 6C2, Canada
Interests: artificial intelligence; machine learning; epidemic model; infectious diseases simulation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sergiy Yakovlev

E-Mail Website
Guest Editor
Institute of Information Technology, Lodz University of Technology, 90-924 Lodz, Poland
Interests: mathematical modeling; optimization of complex systems; combinatorial optimization; packing and covering problems; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue entitled “Artificial Intelligence Applications in Public Health: 2nd Edition”. This Special Issue aims to gather research studies across various disciplines to shed light on the cutting-edge uses of computational techniques and artificial intelligence (AI) in the field of public health.

This Special Issue emphasizes AI’s transformative potential in managing and addressing critical challenges in public health, from disease surveillance, outbreak prediction, and health systems’ optimization, to personalized health interventions. The rapidly expanding capabilities of AI and computation make them increasingly indispensable in public health decision-making, enhancing both efficiency and effectiveness.

The articles collected in this Special Issue will cover a broad spectrum of topics, including, but not limited to, AI-enhanced predictive modeling for disease spread, big data analytics for health trend forecasting, machine learning for patient stratification, and deep learning for image-based diagnostics in public health settings. With this Special Issue, we aim to provide a comprehensive overview of the current state of the art in this field and to inspire innovative future research.

This Special Issue is a call to all researchers, data scientists, public health experts, and policymakers to submit their original research, reviews, case studies, and thought-provoking perspectives that demonstrate the novel uses and potentials of AI and computation in public health.

Dr. Dmytro Chumachenko
Prof. Dr. Sergiy Yakovlev
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. Computation 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 1800 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
  • public health
  • computation
  • disease surveillance
  • predictive modeling
  • health systems optimization
  • public health informatics
  • data-driven medicine

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

17 pages, 1513 KiB  
Article
Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case
by Ivan Izonin, Roman Tkachenko, Nazarii Hovdysh, Oleh Berezsky, Kyrylo Yemets and Ivan Tsmots
Computation 2025, 13(4), 80; https://doi.org/10.3390/computation13040080 - 21 Mar 2025
Viewed by 243
Abstract
In the field of transplantology, where medical decisions are heavily dependent on complex data analysis, the challenge of small data has become increasingly prominent. Transplantology, which focuses on the transplantation of organs and tissues, requires exceptional accuracy and precision in predicting outcomes, assessing [...] Read more.
In the field of transplantology, where medical decisions are heavily dependent on complex data analysis, the challenge of small data has become increasingly prominent. Transplantology, which focuses on the transplantation of organs and tissues, requires exceptional accuracy and precision in predicting outcomes, assessing risks, and tailoring treatment plans. However, the inherent limitations of small datasets present significant obstacles. This paper introduces an advanced input-doubling classifier designed to improve survival predictions for allogeneic bone marrow transplants. The approach utilizes two artificial intelligence tools: the first Probabilistic Neural Network generates output signals that expand the independent attributes of an augmented dataset, while the second machine learning algorithm performs the final classification. This method, based on the cascading principle, facilitates the development of novel algorithms for preparing and applying the enhanced input-doubling technique to classification tasks. The proposed method was tested on a small dataset within transplantology, focusing on binary classification. Optimal parameters for the method were identified using the Dual Annealing algorithm. Comparative analysis of the improved method against several existing approaches revealed a substantial improvement in accuracy across various performance metrics, underscoring its practical benefits Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health: 2nd Edition)
►▼ Show Figures

Figure 1

23 pages, 466 KiB  
Article
COVID-19 Data Analysis: The Impact of Missing Data Imputation on Supervised Learning Model Performance
by Jorge Daniel Mello-Román and Adrián Martínez-Amarilla
Computation 2025, 13(3), 70; https://doi.org/10.3390/computation13030070 - 8 Mar 2025
Viewed by 1637
Abstract
The global COVID-19 pandemic has generated extensive datasets, providing opportunities to apply machine learning for diagnostic purposes. This study evaluates the performance of five supervised learning models—Random Forests (RFs), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Logistic Regression (LR), and Decision Trees [...] Read more.
The global COVID-19 pandemic has generated extensive datasets, providing opportunities to apply machine learning for diagnostic purposes. This study evaluates the performance of five supervised learning models—Random Forests (RFs), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Logistic Regression (LR), and Decision Trees (DTs)—on a hospital-based dataset from the Concepción Department in Paraguay. To address missing data, four imputation methods (Predictive Mean Matching via MICE, RF-based imputation, K-Nearest Neighbor, and XGBoost-based imputation) were tested. Model performance was compared using metrics such as accuracy, AUC, F1-score, and MCC across five levels of missingness. Overall, RF consistently achieved high accuracy and AUC at the highest missingness level, underscoring its robustness. In contrast, SVM often exhibited a trade-off between specificity and sensitivity. ANN and DT showed moderate resilience, yet were more prone to performance shifts under certain imputation approaches. These findings highlight RF’s adaptability to different imputation strategies, as well as the importance of selecting methods that minimize sensitivity–specificity trade-offs. By comparing multiple imputation techniques and supervised models, this study provides practical insights for handling missing medical data in resource-constrained settings and underscores the value of robust ensemble methods for reliable COVID-19 diagnostics. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health: 2nd Edition)
►▼ Show Figures

Figure 1

18 pages, 2813 KiB  
Article
Multimodal Data Fusion for Depression Detection Approach
by Mariia Nykoniuk, Oleh Basystiuk, Nataliya Shakhovska and Nataliia Melnykova
Computation 2025, 13(1), 9; https://doi.org/10.3390/computation13010009 - 2 Jan 2025
Cited by 1 | Viewed by 1931
Abstract
Depression is one of the most common mental health disorders in the world, affecting millions of people. Early detection of depression is crucial for effective medical intervention. Multimodal networks can greatly assist in the detection of depression, especially in situations where in patients [...] Read more.
Depression is one of the most common mental health disorders in the world, affecting millions of people. Early detection of depression is crucial for effective medical intervention. Multimodal networks can greatly assist in the detection of depression, especially in situations where in patients are not always aware of or able to express their symptoms. By analyzing text and audio data, such networks are able to automatically identify patterns in speech and behavior that indicate a depressive state. In this study, we propose two multimodal information fusion networks: early and late fusion. These networks were developed using convolutional neural network (CNN) layers to learn local patterns, a bidirectional LSTM (Bi-LSTM) to process sequences, and a self-attention mechanism to improve focus on key parts of the data. The DAIC-WOZ and EDAIC-WOZ datasets were used for the experiments. The experiments compared the precision, recall, f1-score, and accuracy metrics for the cases of using early and late multimodal data fusion and found that the early information fusion multimodal network achieved higher classification accuracy results. On the test dataset, this network achieved an f1-score of 0.79 and an overall classification accuracy of 0.86, indicating its effectiveness in detecting depression. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health: 2nd 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