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Artificial Intelligence in the Management of the Pandemic
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Artificial Intelligence in the Management of the Pandemic

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 20377

Special Issue Editors

Dr. Alessandro Perrella

E-Mail Website
Guest Editor
VII Department of Infectious Disease and Immunology, AORN Ospedali dei Colli, Naples, Italy
Interests: immunological defects; infectious diseases; chronic inflammatory immunological diseases; big analytic data & eHealth
Special Issues, Collections and Topics in MDPI journals
Dr. Valentina Orlando

E-Mail Website
Guest Editor
Department of Pharmacy, University of Naples Federico II, 80138 Napoli NA, Italy
Interests: big data analytics; drug utilization research; health care decision making; regulatory decision-making; medication adherence
Special Issues, Collections and Topics in MDPI journals
Dr. Pierpaolo Di Micco

E-Mail Website
Guest Editor
UOC Medicina, Fatebenefratelli Hospital of Naples, 80131 Naples, Italy
Interests: immunology; vascular medicine; cardiology; infectious disease; neurology; coagulation and clinical laboratory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and related technologies, despite mainly used in business and social studies, are beginning to be used in healthcare field too. Particularly COVID19 pandemic has boosted up the use of new technologies related to AI rewriting in part the approach to patient care, administrative processes within provider, payer and pharmaceutical organisations as well as healthcare management. While a significant  number of research studies are already available about the application of AI in disease diagnosis, still few data have been published on forecasting alghortim or decisional AI based approaches to the management of pandemic. Aim of this special issue is to offer a wide and technical description of all AI approaches to COVID19 and Healthcare management during pandemic from its beginning to future prospectives, including a "syndemic" point of view too. Original articles  based on local experience on the use of AI, reviews or metanalisys of what already available in litterature would significantly focus on AI and being an interesting input for scientific community

Dr. Alessandro Perrella
Dr. Valentina Orlando
Dr. Pierpaolo Di Micco
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. Healthcare 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 2700 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.

Published Papers (7 papers)

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19 pages, 3006 KiB  
Article
Network Pharmacological Analysis on the Herbal Combinations for Mitigating Inflammation in Respiratory Tracts and Experimental Evaluation
by Dongyeop Jang, Myong Jin Lee, Kang Sub Kim, Chang-Eop Kim, Jong Ho Jung, Minkwan Cho, Bo-Hee Hong, Shin Jung Park and Ki Sung Kang
Healthcare 2023, 11(1), 143; https://doi.org/10.3390/healthcare11010143 - 03 Jan 2023
Cited by 2 | Viewed by 1676
Abstract
The regulation of inflammatory mediators, such as TNF-α, IL-6, IL-1β, and leukotriene B4, could play a crucial role in suppressing inflammatory diseases such as COVID-19. In this study, we investigated the potential mechanisms of drug combinations comprising Ephedrae Herba, Schisandra Fructus, Platycodonis Radix, [...] Read more.
The regulation of inflammatory mediators, such as TNF-α, IL-6, IL-1β, and leukotriene B4, could play a crucial role in suppressing inflammatory diseases such as COVID-19. In this study, we investigated the potential mechanisms of drug combinations comprising Ephedrae Herba, Schisandra Fructus, Platycodonis Radix, and Ginseng Radix; validated the anti-inflammatory effects of these drugs; and determined the optimal dose of the drug combinations. By constructing a herb-compound-target network, associations were identified between the herbs and tissues (such as bronchial epithelial cells and lung) and pathways (such as the TNF, NF-κB, and calcium signaling pathways). The drug combinations exerted anti-inflammatory effects in the RAW264.7 cell line treated with lipopolysaccharide by inhibiting the production of nitric oxide and inflammatory mediators, including TNF-α, IL-6, IL-1β, and leukotriene B4. Notably, the drug combinations inhibited PMA-induced MUC5AC mRNA expression in NCI-H292 cells. A design space analysis was carried out to determine the optimal herbal medicine combinations using the design of experiments and synergy score calculation. Consequently, a combination study of the herbal preparations confirmed their mitigating effect on inflammation in COVID-19. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Management of the Pandemic)
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17 pages, 804 KiB  
Article
Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies
by Jawad Rasheed and Raed M. Shubair
Healthcare 2022, 10(7), 1313; https://doi.org/10.3390/healthcare10071313 - 14 Jul 2022
Cited by 18 | Viewed by 1620
Abstract
The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. [...] Read more.
The global pandemic COVID-19 is still a cause of a health emergency in several parts of the world. Apart from standard testing techniques to identify positive cases, auxiliary tools based on artificial intelligence can help with the identification and containment of the disease. The need for the development of alternative smart diagnostic tools to combat the COVID-19 pandemic has become more urgent. In this study, a smart auxiliary framework based on machine learning (ML) is proposed; it can help medical practitioners in the identification of COVID-19-affected patients, among others with pneumonia and healthy individuals, and can help in monitoring the status of COVID-19 cases using X-ray images. We investigated the application of transfer-learning (TL) networks and various feature-selection techniques for improving the classification accuracy of ML classifiers. Three different TL networks were tested to generate relevant features from images; these TL networks include AlexNet, ResNet101, and SqueezeNet. The generated relevant features were further refined by applying feature-selection methods that include iterative neighborhood component analysis (iNCA), iterative chi-square (iChi2), and iterative maximum relevance–minimum redundancy (iMRMR). Finally, classification was performed using convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Moreover, the study exploited stationary wavelet (SW) transform to handle the overfitting problem by decomposing each image in the training set up to three levels. Furthermore, it enhanced the dataset, using various operations as data-augmentation techniques, including random rotation, translation, and shear operations. The analysis revealed that the combination of AlexNet, ResNet101, SqueezeNet, iChi2, and SVM was very effective in the classification of X-ray images, producing a classification accuracy of 99.2%. Similarly, AlexNet, ResNet101, and SqueezeNet, along with iChi2 and the proposed CNN network, yielded 99.0% accuracy. The results showed that the cascaded feature generator and selection strategies significantly affected the performance accuracy of the classifier. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Management of the Pandemic)
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35 pages, 714 KiB  
Article
Body Language Analysis in Healthcare: An Overview
by Rawad Abdulghafor, Sherzod Turaev and Mohammed A. H. Ali
Healthcare 2022, 10(7), 1251; https://doi.org/10.3390/healthcare10071251 - 04 Jul 2022
Cited by 7 | Viewed by 7389
Abstract
Given the current COVID-19 pandemic, medical research today focuses on epidemic diseases. Innovative technology is incorporated in most medical applications, emphasizing the automatic recognition of physical and emotional states. Most research is concerned with the automatic identification of symptoms displayed by patients through [...] Read more.
Given the current COVID-19 pandemic, medical research today focuses on epidemic diseases. Innovative technology is incorporated in most medical applications, emphasizing the automatic recognition of physical and emotional states. Most research is concerned with the automatic identification of symptoms displayed by patients through analyzing their body language. The development of technologies for recognizing and interpreting arm and leg gestures, facial features, and body postures is still in its early stage. More extensive research is needed using artificial intelligence (AI) techniques in disease detection. This paper presents a comprehensive survey of the research performed on body language processing. Upon defining and explaining the different types of body language, we justify the use of automatic recognition and its application in healthcare. We briefly describe the automatic recognition framework using AI to recognize various body language elements and discuss automatic gesture recognition approaches that help better identify the external symptoms of epidemic and pandemic diseases. From this study, we found that since there are studies that have proven that the body has a language called body language, it has proven that language can be analyzed and understood by machine learning (ML). Since diseases also show clear and different symptoms in the body, the body language here will be affected and have special features related to a particular disease. From this examination, we discovered that it is possible to specialize the features and language changes of each disease in the body. Hence, ML can understand and detect diseases such as pandemic and epidemic diseases and others. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Management of the Pandemic)
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12 pages, 1438 KiB  
Article
Risk of SARS-CoV-2 Infection Breakthrough among the Non-Vaccinated and Vaccinated Population in Italy: A Real-World Evidence Study Based on Big Data
by Alessandro Perrella, Massimo Bisogno, Angelo D’Argenzio, Ugo Trama, Enrico Coscioni and Valentina Orlando
Healthcare 2022, 10(6), 1085; https://doi.org/10.3390/healthcare10061085 - 10 Jun 2022
Cited by 3 | Viewed by 1656
Abstract
SARS-CoV-2 infection after vaccination can occur because COVID-19 vaccines do not offer 100% protection. The study aim was to assess duration of vaccination coverage, disease symptoms and type of hospitalization among non-vaccinated and vaccinated subjects to evaluate the vaccination trend over time. A [...] Read more.
SARS-CoV-2 infection after vaccination can occur because COVID-19 vaccines do not offer 100% protection. The study aim was to assess duration of vaccination coverage, disease symptoms and type of hospitalization among non-vaccinated and vaccinated subjects to evaluate the vaccination trend over time. A retrospective cohort study was carried out among people testing COVID-19 positive in Campania Region using information from the Health Information System of Campania Region (Sinfonia). Vaccination status was assessed considering: no vaccination, partial vaccination and effective vaccination. Univariate and multivariate logistic regression models were constructed to evaluate the association between ICU admissions caused by COVID-19 and gender, age groups and vaccine type. Vaccine coverage duration trends were investigated using segmented linear regression and breakpoint estimations. Vaccination coverage was assessed by analyzing COVID-19 positive subjects in the 9 months after an effective dose vaccination. A significant risk of hospitalization in the ICU was caused by vaccination status: subjects non-vaccinated (OR: 7.14) and partially vaccinated (OR: 3.68) were 3 and 7 times more at risk of hospitalization, respectively, than subjects effectively vaccinated. Regarding subjects with an effective vaccination, the vaccine’s ability to protect against infection in the months following vaccination decreased. The risk of contracting COVID-19 after vaccination was higher 5 months (β = 1441, p < 0.001) and 7 months (β = 3110, p < 0.001) after administration of an effective dose. COVID-19 vaccines were demonstrated to protect from symptomatic infection by significantly reducing hospitalization risk, and their full protection against SARS-CoV-2 was demonstrated to decrease after 5 months regardless of age, gender or vaccine type. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Management of the Pandemic)
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12 pages, 4193 KiB  
Article
Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia
by Song-Quan Ong, Maisarah Binti Mohamed Pauzi and Keng Hoon Gan
Healthcare 2022, 10(6), 994; https://doi.org/10.3390/healthcare10060994 - 27 May 2022
Cited by 6 | Viewed by 2270
Abstract
Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We [...] Read more.
Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investigate the attitudes to the COVID-19 vaccination booster in Malaysia by using sentiment analysis. We retrieved 788 tweets containing COVID-19 vaccine booster keywords and identified the common topics discussed in tweets that related to the booster by using latent Dirichlet allocation (LDA) and performed sentiment analysis to understand the determinants for the sentiments to receiving the vaccination booster in Malaysia. We identified three important LDA topics: (1) type of vaccination booster; (2) effects of vaccination booster; (3) vaccination program operation. The type of vaccination further transformed into attributes of “az”, “pfizer”, “sinovac”, and “mix” for determinants’ assessments. Effect and type of vaccine booster associated stronger than program operation topic for the sentiments, and “pfizer” and “mix” were the strongest determinants of the tweet’s sentiments after the Boruta feature selection and validated from the performance of regression analysis. This study provided a comprehensive workflow to retrieve and identify important healthcare topic from social media. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Management of the Pandemic)
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19 pages, 1625 KiB  
Article
Detection of COVID-19 Based on Chest X-rays Using Deep Learning
by Walaa Gouda, Maram Almurafeh, Mamoona Humayun and Noor Zaman Jhanjhi
Healthcare 2022, 10(2), 343; https://doi.org/10.3390/healthcare10020343 - 10 Feb 2022
Cited by 35 | Viewed by 2904
Abstract
The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease’s spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. [...] Read more.
The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease’s spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 detection using chest X-ray (CXR) images are proposed in this study. Augmenting, enhancing, normalizing, and resizing CXR images to a fixed size are all part of the preprocessing stage. This research proposes a DL method for classifying CXR images based on an ensemble employing multiple runs of a modified version of the Resnet-50. The proposed system is evaluated against two publicly available benchmark datasets that are frequently used by several researchers: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia). The proposed system validates its dominance over existing methods such as VGG or Densnet, with values exceeding 99.63% in many metrics, such as accuracy, precision, recall, F1-score, and Area under the curve (AUC), based on the performance results obtained. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Management of the Pandemic)
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42 pages, 1705 KiB  
Systematic Review
A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022
by KC Santosh, Debasmita GhoshRoy and Suprim Nakarmi
Healthcare 2023, 11(17), 2388; https://doi.org/10.3390/healthcare11172388 - 24 Aug 2023
Cited by 5 | Viewed by 1359
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has [...] Read more.
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Management of the Pandemic)
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