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Computation
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Artificial Intelligence Applications in Public Health
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Artificial Intelligence Applications in Public Health

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

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 10442

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're pleased to announce a forthcoming Special Issue titled “Artificial Intelligence Applications in Public Health”. 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 of 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
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

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Related Special Issue

Published Papers (6 papers)

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Research

22 pages, 1650 KiB  
Article
Interpretable Conversation Routing via the Latent Embeddings Approach
by Daniil Maksymenko and Oleksii Turuta
Computation 2024, 12(12), 237; https://doi.org/10.3390/computation12120237 - 1 Dec 2024
Viewed by 356
Abstract
Large language models (LLMs) are quickly implemented to answer question and support systems to automate customer experience across all domains, including medical use cases. Models in such environments should solve multiple problems like general knowledge questions, queries to external sources, function calling and [...] Read more.
Large language models (LLMs) are quickly implemented to answer question and support systems to automate customer experience across all domains, including medical use cases. Models in such environments should solve multiple problems like general knowledge questions, queries to external sources, function calling and many others. Some cases might not even require a full-on text generation. They possibly need different prompts or even different models. All of it can be managed by a routing step. This paper focuses on interpretable few-shot approaches for conversation routing like latent embeddings retrieval. The work here presents a benchmark, a sorrow analysis, and a set of visualizations of the way latent embeddings routing works for long-context conversations in a multilingual, domain-specific environment. The results presented here show that the latent embeddings router is able to achieve performance on the same level as LLM-based routers with additional interpretability and higher level of control over model decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)
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20 pages, 1645 KiB  
Article
Classification of Acoustic Tones and Cardiac Murmurs Based on Digital Signal Analysis Leveraging Machine Learning Methods
by Nataliya Shakhovska and Ivan Zagorodniy
Computation 2024, 12(10), 208; https://doi.org/10.3390/computation12100208 - 17 Oct 2024
Viewed by 827
Abstract
Heart murmurs are abnormal heart sounds that can indicate various heart diseases. Although traditional auscultation methods are effective, they depend more on specialists’ knowledge, making it difficult to make an accurate diagnosis. This paper presents a machine learning-based framework for the classification of [...] Read more.
Heart murmurs are abnormal heart sounds that can indicate various heart diseases. Although traditional auscultation methods are effective, they depend more on specialists’ knowledge, making it difficult to make an accurate diagnosis. This paper presents a machine learning-based framework for the classification of acoustic sounds and heart murmurs using digital signal analysis. Using advanced machine learning algorithms, we aim to improve the accuracy, speed, and accessibility of heart murmur detection. The proposed method includes feature extraction from digital auscultatory recordings, preprocessing using signal processing techniques, and classification using state-of-the-art machine learning models. We evaluated the performance of different machine learning algorithms, such as convolutional neural networks (CNNs), random forests (RFs) and support vector machines (SVMs), on a selected heart noise dataset. The results show that our framework achieves high accuracy in differentiating normal heart sounds from different types of heart murmurs and provides a robust tool for clinical decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)
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15 pages, 2268 KiB  
Article
Enhanced Input-Doubling Method Leveraging Response Surface Linearization to Improve Classification Accuracy in Small Medical Data Processing
by Ivan Izonin, Roman Tkachenko, Pavlo Yendyk, Iryna Pliss, Yevgeniy Bodyanskiy and Michal Gregus
Computation 2024, 12(10), 203; https://doi.org/10.3390/computation12100203 - 11 Oct 2024
Viewed by 757
Abstract
Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do [...] Read more.
Currently, the tasks of intelligent data analysis in medicine are becoming increasingly common. Existing artificial intelligence tools provide high effectiveness in solving these tasks when analyzing sufficiently large datasets. However, when there is very little training data available, current machine learning methods do not ensure adequate classification accuracy or may even produce inadequate results. This paper presents an enhanced input-doubling method for classification tasks in the case of limited data analysis, achieved via expanding the number of independent attributes in the augmented dataset with probabilities of belonging to each class of the task. The authors have developed an algorithmic implementation of the improved method using two Naïve Bayes classifiers. The method was modeled on a small dataset for cardiovascular risk assessment. The authors explored two options for the combined use of Naïve Bayes classifiers at both stages of the method. It was found that using different methods at both stages potentially enhances the accuracy of the classification task. The results of the improved method were compared with a range of existing methods used for solving the task. It was demonstrated that the improved input-doubling method achieved the highest classification accuracy based on various performance indicators. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)
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10 pages, 1200 KiB  
Article
Impact of Ukrainian Refugees on the COVID-19 Pandemic Dynamics after 24 February 2022
by Igor Nesteruk and Paul Brown
Computation 2024, 12(4), 70; https://doi.org/10.3390/computation12040070 - 3 Apr 2024
Cited by 1 | Viewed by 1321
Abstract
The full-scale invasion of Ukraine caused an unprecedented number of refugees after 24 February 2022. To estimate the influence of this humanitarian disaster on the COVID-19 pandemic dynamics, the smoothed daily numbers of cases in Ukraine, the UK, Poland, Germany, the Republic of [...] Read more.
The full-scale invasion of Ukraine caused an unprecedented number of refugees after 24 February 2022. To estimate the influence of this humanitarian disaster on the COVID-19 pandemic dynamics, the smoothed daily numbers of cases in Ukraine, the UK, Poland, Germany, the Republic of Moldova, and in the whole world were calculated and compared with values predicted by the generalized SIR model. In March 2022, the increase in the smoothed number of new cases in the UK, Germany, and worldwide was visible. A simple formula to estimate the effective reproduction number based on the smoothed accumulated numbers of cases is proposed. The results of calculations agree with the figures presented by John Hopkins University and demonstrate a short-term growth in the reproduction number in the UK, Poland, Germany, Moldova, and worldwide in March 2022. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)
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62 pages, 14899 KiB  
Article
Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior
by Nirmalya Thakur, Shuqi Cui, Kesha A. Patel, Nazif Azizi, Victoria Knieling, Changhee Han, Audrey Poon and Rishika Shah
Computation 2023, 11(11), 234; https://doi.org/10.3390/computation11110234 - 17 Nov 2023
Cited by 1 | Viewed by 3878
Abstract
During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus [...] Read more.
During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on 4 October 2023 with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on 3 October 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on 4 October 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time-series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches on Google on 4 October 2023. Finally, the correlation between zombie-related searches in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)
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26 pages, 3568 KiB  
Article
Epidemiological Implications of War: Machine Learning Estimations of the Russian Invasion’s Effect on Italy’s COVID-19 Dynamics
by Dmytro Chumachenko, Tetiana Dudkina, Tetyana Chumachenko and Plinio Pelegrini Morita
Computation 2023, 11(11), 221; https://doi.org/10.3390/computation11110221 - 4 Nov 2023
Viewed by 2136
Abstract
Background: The COVID-19 pandemic has profoundly transformed the global scenario, marked by overwhelming infections, fatalities, overburdened healthcare infrastructures, economic upheavals, and significant lifestyle modifications. Concurrently, the Russian full-scale invasion of Ukraine on 24 February 2022, triggered a severe humanitarian and public health crisis, [...] Read more.
Background: The COVID-19 pandemic has profoundly transformed the global scenario, marked by overwhelming infections, fatalities, overburdened healthcare infrastructures, economic upheavals, and significant lifestyle modifications. Concurrently, the Russian full-scale invasion of Ukraine on 24 February 2022, triggered a severe humanitarian and public health crisis, leading to healthcare disruptions, medical resource shortages, and heightened emergency care needs. Italy emerged as a significant refuge for displaced Ukrainians during this period. Aim: This research aims to discern the impact of the Russian full-scale invasion of Ukraine on the COVID-19 transmission dynamics in Italy. Materials and Methods: The study employed advanced simulation methodologies, particularly those integrating machine learning, to model the pandemic’s trajectory. The XGBoost algorithm was adopted to construct a predictive model for the COVID-19 epidemic trajectory in Italy. Results: The model demonstrated a commendable accuracy of 86.03% in forecasting new COVID-19 cases in Italy over 30 days and an impressive 96.29% accuracy in estimating fatalities. When applied to the initial 30 days following the escalation of the conflict (24 February 2022, to 25 March 2022), the model’s projections suggested that the influx of Ukrainian refugees into Italy did not significantly alter the country’s COVID-19 epidemic course. Discussion: While simulation methodologies have been pivotal in the pandemic response, their accuracy is intrinsically linked to data quality, assumptions, and modeling techniques. Enhancing these methodologies can further their applicability in future public health emergencies. The findings from the model underscore that external geopolitical events, such as the mass migration from Ukraine, did not play a determinative role in Italy’s COVID-19 epidemic dynamics during the study period. Conclusion: The research provides empirical evidence negating a substantial influence of the Ukrainian refugee influx due to the Russian full-scale invasion on the COVID-19 epidemic trajectory in Italy. The robust performance of the developed model affirms its potential value in public health analyses. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Public Health)
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