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Electronics
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Applications of Artificial Intelligence for Health
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Applications of Artificial Intelligence for Health

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2024

Special Issue Editors

Prof. Dr. Juan Antonio Ortega

E-Mail Website
Guest Editor
Department of Computer Science and Languages, Universidad de Sevilla, 41012 Sevilla, Spain
Interests: eHealth; digital twins; artificial intelligence, assisted and information systems, energy efficiency, and their application to real systems; smart sensors
Dr. Cecilio Angulo

E-Mail Website
Guest Editor
IDEAI-UPC Research Centre on Intelligent Data Science and Artificial Intelligence, Universitat Politècnica de Caytalunya, 08034 Barcelona, Spain
Interests: cognitive robotics; artificial intelligence and control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Gonzalez Abril

E-Mail Website
Guest Editor
Applied Economics I Department, Universidad de Sevilla, 41018 Sevilla, Spain
Interests: AI; machine learning; statistics; econometric

Special Issue Information

Dear Colleagues,

In recent years, there has been a huge proliferation of solutions that store and process personal health data, inferring knowledge from mobile health apps to smart wearable sensors.

The incorporation of data obtained from several sources enables the application of artificial intelligence techniques for the analysis of these data and the search for patterns.

Sources of medical data in health services are causing important concerns, the main being privacy and legal issues when sharing and reporting health information from patients. However, an accurate diagnosis will depend on the quantity and quality of the information about a patient, as well as extensive medical knowledge.

On the other hand, health information systems must clearly incorporate the sharing of data from heterogeneous sources, enabling the creation of multidisciplinary work teams that work with them. In these teams, medical professionals share information and obtain recommendations from data scientists, computer programmers, mathematicians, and statisticians.

In this Special Issue, we are interested in incorporating the latest advances in the application of artificial intelligence to the world of health. The works that will have a place in this issue will be both those focused on the health systems themselves, as well as the techniques from artificial intelligence that are applied to health, such as deep learning, generative adversarial networks, digital twins, and others related solutions. Similarly, the techniques for incorporating health data, which address aspects such as quality, safety, interoperability, techniques to incorporate missing values, etc. are welcome for submission.

Prof. Dr. Juan Antonio Ortega
Dr. Cecilio Angulo
Prof. Dr. Luis Gonzalez Abril 
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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
  • healthcare
  • eHealth
  • clinical solutions
  • health systems
  • machine learning
  • health innovation
  • health application
  • digital health
  • digital twins
  • generative adversarial networks
  • health data

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Published Papers (1 paper)

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Research

29 pages, 4423 KiB  
Article
A Synthesis of Pulse Influenza Vaccination Policies Using an Efficient Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA)
by Asma Khalil Alkhamis and Manar Hosny
Electronics 2022, 11(22), 3711; https://doi.org/10.3390/electronics11223711 - 13 Nov 2022
Cited by 5 | Viewed by 1448
Abstract
Seasonal influenza (also known as flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. [...] Read more.
Seasonal influenza (also known as flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to the WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of the influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results indicate that optimal vaccination campaigns with compromise tradeoffs between the two conflicting objectives can be designed effectively using CENSGA, providing policymakers with a number of alternatives to accommodate the best strategies. The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence for Health)
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