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Farhat F, Sohail SS, Alam MT, Ubaid S, Shakil, Ashhad M, Madsen DØ. COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control. Front Artif Intell 2023; 6:1266560. [PMID: 38028660 PMCID: PMC10663297 DOI: 10.3389/frai.2023.1266560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/02/2023] [Indexed: 12/01/2023] Open
Abstract
COVID-19 has brought significant changes to our political, social, and technological landscape. This paper explores the emergence and global spread of the disease and focuses on the role of Artificial Intelligence (AI) in containing its transmission. To the best of our knowledge, there has been no scientific presentation of the early pictorial representation of the disease's spread. Additionally, we outline various domains where AI has made a significant impact during the pandemic. Our methodology involves searching relevant articles on COVID-19 and AI in leading databases such as PubMed and Scopus to identify the ways AI has addressed pandemic-related challenges and its potential for further assistance. While research suggests that AI has not fully realized its potential against COVID-19, likely due to data quality and diversity limitations, we review and identify key areas where AI has been crucial in preparing the fight against any sudden outbreak of the pandemic. We also propose ways to maximize the utilization of AI's capabilities in this regard.
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Affiliation(s)
- Faiza Farhat
- Department of Zoology, Aligarh Muslim University, Aligarh, India
| | | | - Mohammed Talha Alam
- Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India
| | - Syed Ubaid
- Faculty of Electronic and Information Technology, Warsaw University of Technology, Warsaw, Poland
| | - Shakil
- Faculty of Electronic and Information Technology, Warsaw University of Technology, Warsaw, Poland
| | - Mohd Ashhad
- Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India
| | - Dag Øivind Madsen
- USN School of Business, University of South-Eastern Norway, Hønefoss, Norway
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Butt MJ, Malik AK, Qamar N, Yar S, Malik AJ, Rauf U. A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media. SENSORS (BASEL, SWITZERLAND) 2023; 23:5543. [PMID: 37420714 DOI: 10.3390/s23125543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/04/2023] [Accepted: 06/07/2023] [Indexed: 07/09/2023]
Abstract
Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.
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Affiliation(s)
- Muhammad Junaid Butt
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan
| | - Ahmad Kamran Malik
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan
| | - Nafees Qamar
- School of Health and Behavioral Sciences, Bryant University, Smithfield, RI 02917, USA
| | - Samad Yar
- Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad 45550, Pakistan
| | - Arif Jamal Malik
- Department of Software Engineering, Foundation University, Islamabad 44000, Pakistan
| | - Usman Rauf
- Department of Mathematics and Computer Science, Mercy College, Dobbs Ferry, NY 10522, USA
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Strengthening capacities of multidisciplinary professionals to apply data science in public health: Experience of an international graduate diploma program in Peru. Int J Med Inform 2023; 169:104913. [PMID: 36410127 DOI: 10.1016/j.ijmedinf.2022.104913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022]
Abstract
Nowadays it is necessary to strengthen health information systems and data-based solutions. However, there are few graduate training programs in Peru to use tools and methods of data science applied in public health. This article describes the development process and the initial assessment regarding the experience of the participants in an international multidisciplinary diploma in data intelligence for pandemics and epidemics preparedness, which was carried out from January to May 2021. The diploma was structured in 7 modules and 40 Peruvian professionals participated, of which 11 (27.5%) were women, and 16 (40%) came from regions outside of Lima and Callao. We discussed the need to strengthen institutional and health professionals' capacity to adequately manage large volumes of data, information, and knowledge through the application of emerging technologies to optimize data management processes to improve decision-making in health.
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Wu TC, Ho CTB. A Narrative Review of Innovative Responses During the COVID-19 Pandemic in 2020. Int J Public Health 2022; 67:1604652. [PMID: 36570874 PMCID: PMC9772050 DOI: 10.3389/ijph.2022.1604652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
Objectives: The coronavirus disease 2019 (COVID-19) pandemic presented unprecedented challenges to healthcare systems worldwide. While existing studies on innovation have typically focused on technology, health providers still only have a vague understanding of the features of emergency responses during resource exhaustion in the early stage of a pandemic. Thus, a better understanding of innovative responses by healthcare systems during a crisis is urgently needed. Methods: Using content analysis, this narrative review examined articles on innovative responses during the COVID-19 pandemic that were published in 2020. Results: A total of 613 statements about innovative responses were identified from 296 articles and were grouped under the following thematic categories: medical care (n = 273), workforce education (n = 144), COVID-19 surveillance (n = 84), medical equipment (n = 59), prediction and management (n = 34), and governance (n = 19). From the four types of innovative responses extracted, technological innovation was identified as the major type of innovation during the COVID-19 pandemic, followed by process innovations, frugal innovation, and repurposing. Conclusion: Our review provides insights into the features, types, and evolution of innovative responses during the COVID-19 pandemic. This review can help health providers and society show better and quicker responses in resource-constrained conditions in future pandemics.
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Affiliation(s)
- Tzu-Chi Wu
- Institute of Technology Management, National Chung-Hsing University, Taichung, Taiwan,Department of Emergency Medicine, Show Chwan Memorial Hospital, Changua, Taiwan,*Correspondence: Tzu-Chi Wu,
| | - Chien-Ta Bruce Ho
- Institute of Technology Management, National Chung-Hsing University, Taichung, Taiwan
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Chiu CC, Wu CM, Chien TN, Kao LJ, Li C, Jiang HL. Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure. J Clin Med 2022; 11:6460. [PMID: 36362686 PMCID: PMC9659015 DOI: 10.3390/jcm11216460] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 08/31/2023] Open
Abstract
Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients' conditions by healthcare professionals but allow for a more optimal use of healthcare resources.
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Affiliation(s)
- Chih-Chou Chiu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chung-Min Wu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Te-Nien Chien
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Ling-Jing Kao
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chengcheng Li
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Han-Ling Jiang
- Alliance Manchester Business School, University of Manchester, Manchester M15 6PB, UK
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Lainjo B. The Enigmatic COVID-19 Vulnerabilities and the Invaluable Artificial Intelligence (AI). J Multidiscip Healthc 2021; 14:2361-2372. [PMID: 34475763 PMCID: PMC8407667 DOI: 10.2147/jmdh.s321751] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/16/2021] [Indexed: 11/24/2022] Open
Abstract
The objective of the study is to conduct an exploratory review of the Covid-19 pandemic by focusing on the theme of Covid-19 pandemic morbidity and mortality, considering the dynamics of artificial intelligence and quality of life (QOL). The methods used in this research paper include a review of literature, anecdotal evidence, and reports on the morbidity of COVID-19, including the scope of its devastating effects in different countries such as the US, Africa, UK, China, and Brazil, among others. The findings of this study suggested that the devastating effects of the coronavirus are felt across different vulnerable populations. These include the elderly, front-line workers, marginalized communities, visible minorities, and more. The challenge in Africa is especially daunting because of inadequate infrastructure, and financial and human resources, among others. Besides, AI technology is being successfully used by scientists to enhance the development process of vaccines and drugs. However, its usage in other stages of the pandemic has not been adequately explored. Ultimately, it has been concluded that the effects of the Covid-19 are producing unprecedented and catastrophic outcomes in many countries. With a few exceptions, the common and current intervention approach is driven by many factors, including the compilation of relevant reliable and compelling data sets. On a positive note, the compelling trailblazing and catalytic contributions of AI towards the rapid discovery of COVID-19 vaccines are a good indication of future technological innovations and their effectiveness.
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Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126429. [PMID: 34198547 PMCID: PMC8296243 DOI: 10.3390/ijerph18126429] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 12/12/2022]
Abstract
The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.
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