Artificial Intelligence Applications in Public Health
A special issue of Computation (ISSN 2079-3197).
Deadline for manuscript submissions: 31 May 2024 | Viewed by 4895
Special Issue Editors
2. Ubiquitous Health Technologies Lab, University of Waterloo, Waterloo, ON, Canada
Interests: artificial intelligence; machine learning; epidemic model; infectious diseases simulation
Special Issues, Collections and Topics in MDPI journals
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
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Resilient-aware MLOps for AI-based Medical Diagnostic Systems
Authors: Viacheslav Moskalenko; Vyacheslav Kharchenko
Affiliation: Computer Science Department, Sumy State University, Sumy, Ukraine; Head of Department of Computer Systems, Networks and Cybersecurity National Aerospace University "KhAI", Kharkiv, Ukraine
Abstract: The healthcare sector demands a higher degree of responsibility, trustworthiness, and accountability when implementing AI systems. Machine learning operations (MLOps) for AI-based medical diagnostic systems are primarily focused on aspects such as data quality and confidentiality, bias reduction, model deployment, performance monitoring, and continuous improvement. However, MLOps techniques do not take into account the need to provide resilience to disturbances such as adversarial attacks, including fault injections, and drift, including out-of-distribution. Resilience is defined as the ability to absorb disturbances, detect disturbances (or estimate uncertainty), degrade gracefully, and adapt models quickly without losing (forgetting) the experience accumulated in the models.
The subject of study is the methods of ensuring the resilience of AI systems as a component of MLOps for AI-based Medical Diagnostic Systems. The goal is to improve the MLOPs methodology for AI-based Medical Diagnostic Systems by identifying characteristic disturbances in healthcare and developing appropriate methods to ensure resilience as part of MLOPs. The paper analyzes and systematizes the disturbing influences on Medical Diagnostic Systems, DevOPS methods, and methods for ensuring the resilience of AI systems. In addition, the positive effect of the implementation of Resilient-aware MLOps in Medical Diagnostic Systems was experimentally confirmed on the the task of classifying medical images into malignant and benign classes. The study used large, publicly available clinical datasets such as BreakHis, BraTS, NIH-Xray and COVID-19 X-ray in different scenarios of disturbance exposure.
Title: Impact of Ukrainian refugees on the COVID-19 pandemic dynamics after February 24, 2022
Authors: Igor Nesteruk; Paul Brown
Affiliation: Institute of Hydromechanics, National Academy of Sciences of Ukraine, Kyiv, Ukraine; University of Warwick, Coventry, UK
Abstract: On February 24, 2022 Russia started the full-scale invasion of Ukraine, which caused an unprecedented number of refugees. To estimate the influence of this humanitarian disaster on the COVID-19 pandemic dynamics, the averaged daily numbers of cases and reproduction numbers in Ukraine, the UK, Poland, Germany, the Republic of Moldova, and in the whole world were calculated for the period February-April 2022. The registered numbers of cases were compared with ones calculated with the use of the generalized SIR-model and corresponding parameter identification procedure for the previous epidemic waves in Ukraine, Poland, Germany, and the world. Since before February 24, 2022 the estimation of the number of infectious persons per capita in Ukraine 3.6 times exceeded the global figure, the increase of the number the new cases and the pandemic duration was expected. In March 2022 the increase of the averaged number of new cases in Germany, and worldwide occured. A simple formula to estimate the effective reproduction number based on the smoothed accumulated numbers of cases was proposed. The results of calculations agree with the figures presented by John Hopkins University and demonstrate a short-term growth of the reproduction number in Poland, Germany, Moldova, and worldwide in March 2022.
