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Computers
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Future Systems Based on Healthcare 5.0 for Pandemic Preparedness
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Future Systems Based on Healthcare 5.0 for Pandemic Preparedness

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 9658

Special Issue Editors

Dr. Radhya Sahal

E-Mail Website1 Website2
Guest Editor
School of Computer Science and Information Technology, University College Cork, T12 R229 Cork, Ireland
Interests: digital twins; blockchain; Industry 4.0/5.0; smart manufacturing; Healthcare 4.0/5.0; IoT; big data; stream processing; collaborative systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xuhui Chen

E-Mail Website
Guest Editor
College of Aeronautics & Engineering, Kent State University, Kent, OH 44240, USA
Interests: security and privacy in big data analytics (machine learning, cloud computing); system design; internet of things (IoT); smart health; cyber–physical systems; wireless networks
Special Issues, Collections and Topics in MDPI journals
Dr. Hager Saleh

E-Mail Website
Guest Editor
Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
Interests: big data; artificial intelligence; healthcare; machine learning; deep learning; natural language processing (NLP)

Special Issue Information

Dear Colleagues,

Increased population density, our growing capacity to travel across the globe, environmental changes, infectious diseases that jump from animals to humans, etc. probably mean that COVID-19 will not be the last pandemic in our lifetime. In fact, the risk of a new pandemic is higher now than ever before. COVID-19 has pointed out the lack of smart systems in healthcare organizations for pandemic response (e.g., early reporting of the risk of a pandemic outbreak). Better data drive smarter and earlier decisions, and the combination of the advanced technologies of Healthcare 5.0, including nanotechnology, 5G technologies, drone technology, blockchain, digital twins, robotics, big data, IoT, AI, and cloud computing, has significant advantages in the design of smart systems for pandemic preparedness. Although there has been significant progress toward smart and connected healthcare systems during the pandemic, more research innovation, dissemination, and technologies are needed to unbundle new opportunities and move toward adopting Healthcare 5.0 for pandemic preparedness to save human lives.

This Special Issue of Computers presents cutting-edge research and commentaries to explore the future of adopting Healthcare 5.0 technologies for pandemic preparedness. Researchers, developers, and industry practitioners working in this area are invited to present their views and research work on the current pandemic preparedness trends.

Therefore, the suggested topics of interest for the Special Issue include but are not limited to:

  • Pandemic alerting frameworks and systems;
  • Applications for pandemic preparedness;
  • IoT solutions for pandemic preparedness;
  • Blockchain solutions for pandemic preparedness;
  • Collaborative solutions based on Healthcare 5.0 for pandemic preparedness;
  • Digital twins/digital twin collaboration solutions for pandemic preparedness;
  • Federated learning, machine learning, and deep learning for pandemic preparedness;
  • Personalized COVID-19 medicine;
  • COVID-19 survivor follow-up care;
  • Robot collaboration for contactless systems to combat the pandemic outbreak;
  • Drone collaboration to combat the pandemic outbreak;
  • Cloud computing;
  • Big data/healthcare data;
  • 5/6G roles for pandemic response;
  • Human-in-the-loop AI for pandemic response;
  • Explainable AI for diagnosis;
  • Smart decision making for pandemic response;
  • Remote patient care for combating the pandemic outbreak.

Dr. Radhya Sahal
Dr. Xuhui Chen
Dr. Hager Saleh
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. Computers 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

  • pandemic alerting
  • IoT
  • blockchain
  • digital twins/digital twin collaboration
  • personal digital twins/human digital twins
  • federated learning, machine learning, deep learning
  • personalized COVID-19 medicine
  • COVID-19 survivor follow-up care
  • robot collaboration
  • drone collaboration
  • cloud computing
  • big data
  • 5/6G
  • human-in-the-loop AI for healthcare
  • explainable AI
  • smart decision making
  • remote patient care

Published Papers (3 papers)

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Research

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22 pages, 1156 KiB  
Article
Cervical Cancer Diagnosis Using Stacked Ensemble Model and Optimized Feature Selection: An Explainable Artificial Intelligence Approach
by Abdulaziz AlMohimeed, Hager Saleh, Sherif Mostafa, Redhwan M. A. Saad and Amira Samy Talaat
Computers 2023, 12(10), 200; https://doi.org/10.3390/computers12100200 - 07 Oct 2023
Viewed by 1612
Abstract
Cervical cancer affects more than half a million women worldwide each year and causes over 300,000 deaths. The main goals of this paper are to study the effect of applying feature selection methods with stacking models for the prediction of cervical cancer, propose [...] Read more.
Cervical cancer affects more than half a million women worldwide each year and causes over 300,000 deaths. The main goals of this paper are to study the effect of applying feature selection methods with stacking models for the prediction of cervical cancer, propose stacking ensemble learning that combines different models with meta-learners to predict cervical cancer, and explore the black-box of the stacking model with the best-optimized features using explainable artificial intelligence (XAI). A cervical cancer dataset from the machine learning repository (UCI) that is highly imbalanced and contains missing values is used. Therefore, SMOTE-Tomek was used to combine under-sampling and over-sampling to handle imbalanced data, and pre-processing steps are implemented to hold missing values. Bayesian optimization optimizes models and selects the best model architecture. Chi-square scores, recursive feature removal, and tree-based feature selection are three feature selection techniques that are applied to the dataset For determining the factors that are most crucial for predicting cervical cancer, the stacking model is extended to multiple levels: Level 1 (multiple base learners) and Level 2 (meta-learner). At Level 1, stacking (training and testing stacking) is employed for combining the output of multi-base models, while training stacking is used to train meta-learner models at level 2. Testing stacking is used to evaluate meta-learner models. The results showed that based on the selected features from recursive feature elimination (RFE), the stacking model has higher accuracy, precision, recall, f1-score, and AUC. Furthermore, To assure the efficiency, efficacy, and reliability of the produced model, local and global explanations are provided. Full article
(This article belongs to the Special Issue Future Systems Based on Healthcare 5.0 for Pandemic Preparedness)
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16 pages, 292 KiB  
Article
Factors Affecting mHealth Technology Adoption in Developing Countries: The Case of Egypt
by Ghada Refaat El Said
Computers 2023, 12(1), 9; https://doi.org/10.3390/computers12010009 - 28 Dec 2022
Cited by 4 | Viewed by 2249
Abstract
Mobile health apps are seeing rapid growth in the potential to improve access to healthcare services for disadvantaged communities, while enhancing the efficiency of the healthcare delivery value chain. Still, the adoption of mHealth apps is relatively low, especially in developing countries. In [...] Read more.
Mobile health apps are seeing rapid growth in the potential to improve access to healthcare services for disadvantaged communities, while enhancing the efficiency of the healthcare delivery value chain. Still, the adoption of mHealth apps is relatively low, especially in developing countries. In Egypt, an initiative for national-level healthcare coverage was launched in 2021, accompanied by a rise in mHealth start-ups. However, many of these projects did not progress beyond the pilot stage, with very little known about the antecedents of mHealth adoption for the Egyptian user. Semi-structured interviews were conducted with 22 Egyptians, aiming to uncover factors affecting the use of mHealth apps for Egyptian citizens. Some of these factors were introduced by previous studies, such as Perceived Service Quality, Perceived Risk, Perceived Ease of Use, and Trust. Others were not well established in the mHealth research strand, such as Perceived Reputation and Perceived Familiarity, while Governance, Personalized Experience, Explain-ability, Interaction, Language, and Cultural Issues, are novel factors introduced by the current research. The effect of these suggested independent variables on the willingness to adopt mHealth apps was validated using a survey administered to 150 Egyptians, confirming the significant positive effect of most of these factors on mHealth adoption in Egypt. This research contributes to methodology by introducing novel constructs in the mHealth research context, which might be specific to the target developing country. Practical implications were suggested for designers and healthcare service providers might increase the adoption of their apps in developing countries, such as Egypt. Full article
(This article belongs to the Special Issue Future Systems Based on Healthcare 5.0 for Pandemic Preparedness)

Review

Jump to: Research

19 pages, 3628 KiB  
Review
Deep Learning for Parkinson’s Disease Diagnosis: A Short Survey
by Mohamed Shaban
Computers 2023, 12(3), 58; https://doi.org/10.3390/computers12030058 - 07 Mar 2023
Cited by 6 | Viewed by 4907
Abstract
Parkinson’s disease (PD) is a serious movement disorder that may eventually progress to mild cognitive dysfunction (MCI) and dementia. According to the Parkinson’s foundation, one million Americans were diagnosed with PD and almost 10 million individuals suffer from the disease worldwide. An early [...] Read more.
Parkinson’s disease (PD) is a serious movement disorder that may eventually progress to mild cognitive dysfunction (MCI) and dementia. According to the Parkinson’s foundation, one million Americans were diagnosed with PD and almost 10 million individuals suffer from the disease worldwide. An early and precise clinical diagnosis of PD will ensure an early initiation of effective therapeutic treatments, which will potentially slow down the progression of the disease and improve the quality of life for patients and their caregivers. Machine and deep learning are promising technologies that may assist and support clinicians in providing an objective and reliable diagnosis of the disease based upon significant and unique features identified from relevant medical data. In this paper, the author provides a comprehensive review of the artificial intelligence techniques that were recently proposed during the period from 2016 to 2022 for the screening and staging of PD as well as the identification of the biomarkers of the disease based on Electroencephalography (EEG), Magnetic Resonance Imaging (MRI), speech tests, handwriting exams, and sensory data. In addition, the author highlights the current and future trends for PD diagnosis based machine and deep learning and discusses the limitations, challenges, potential future solutions, and recommendations for a reliable application of machine and deep learning for PD detection and screening. Full article
(This article belongs to the Special Issue Future Systems Based on Healthcare 5.0 for Pandemic Preparedness)
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