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Ahn H, Lee H. Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:6150-6166. [PMID: 38872573 DOI: 10.3934/mbe.2024270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control. This study aims to predict the transmission patterns of COVID-19 using machine learning, such as support vector machine, random forest, and XGBoost, using confirmed cases, death cases, and imported cases, respectively. The study categorizes the transmission trends into the three groups: L0 (decrease), L1 (maintain), and L2 (increase). We develop the risk index function to quantify changes in the transmission trends, which is applied to the classification of machine learning. A high accuracy is achieved when estimating the transmission trends for the confirmed cases (91.5-95.5%), death cases (85.6-91.8%), and imported cases (77.7-89.4%). Notably, the confirmed cases exhibit a higher level of accuracy compared to the data on the deaths and imported cases. L2 predictions outperformed L0 and L1 in all cases. Predicting L2 is important because it can lead to new outbreaks. Thus, this robust L2 prediction is crucial for the timely implementation of control policies for the management of transmission dynamics.
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Affiliation(s)
- Hyeonjeong Ahn
- Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Hyojung Lee
- Department of Statistics, Kyungpook National University, Daegu 41566, Republic of Korea
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Hamar Á, Mohammed D, Váradi A, Herczeg R, Balázsfalvi N, Fülesdi B, László I, Gömöri L, Gergely PA, Kovacs GL, Jáksó K, Gombos K. COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm. Sci Rep 2024; 14:11941. [PMID: 38789490 PMCID: PMC11126653 DOI: 10.1038/s41598-024-62791-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/19/2024] [Indexed: 05/26/2024] Open
Abstract
The emergence of newer SARS-CoV-2 variants of concern (VOCs) profoundly changed the ICU demography; this shift in the virus's genotype and its correlation to lethality in the ICUs is still not fully investigated. We aimed to survey ICU patients' clinical and laboratory parameters in correlation with SARS-CoV-2 variant genotypes to lethality. 503 COVID-19 ICU patients were included in our study beginning in January 2021 through November 2022 in Hungary. Furthermore, we implemented random forest (RF) as a potential predictor regarding SARS-CoV-2 lethality among 649 ICU patients in two ICU centers. Survival analysis and comparison of hypertension (HT), diabetes mellitus (DM), and vaccination effects were conducted. Logistic regression identified DM as a significant mortality risk factor (OR: 1.55, 95% CI 1.06-2.29, p = 0.025), while HT showed marginal significance. Additionally, vaccination demonstrated protection against mortality (p = 0.028). RF detected lethality with 81.42% accuracy (95% CI 73.01-88.11%, [AUC]: 91.6%), key predictors being PaO2/FiO2 ratio, lymphocyte count, and chest Computed Tomography Severity Score (CTSS). Although a smaller number of patients require ICU treatment among Omicron cases, the likelihood of survival has not proportionately increased for those who are admitted to the ICU. In conclusion, our RF model supports more effective clinical decision-making among ICU COVID-19 patients.
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Affiliation(s)
- Ágoston Hamar
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Daryan Mohammed
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Alex Váradi
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
- Institute of Metagenomics, University of Debrecen, Debrecen, Hungary
| | - Róbert Herczeg
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Norbert Balázsfalvi
- Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary
| | - Béla Fülesdi
- Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary
| | - István László
- Department of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary
| | - Lídia Gömöri
- Doctoral School of Neuroscience, University of Debrecen, Debrecen, Hungary
| | | | - Gabor Laszlo Kovacs
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary
| | - Krisztián Jáksó
- Department of Anaesthesiology and Intensive Care, Clinical Centre, University of Pécs, Pécs, Hungary
| | - Katalin Gombos
- Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary.
- Molecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary.
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Viderman D, Kotov A, Popov M, Abdildin Y. Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review. Int J Med Inform 2024; 182:105308. [PMID: 38091862 DOI: 10.1016/j.ijmedinf.2023.105308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/20/2023] [Accepted: 12/03/2023] [Indexed: 01/07/2024]
Abstract
INTRODUCTION Since the beginning of the COVID-19 pandemic, numerous machine and deep learning (MDL) methods have been proposed in the literature to analyze patient physiological data. The objective of this review is to summarize various aspects of these methods and assess their practical utility for predicting various clinical outcomes. METHODS We searched PubMed, Scopus, and Cochrane Library, screened and selected the studies matching the inclusion criteria. The clinical analysis focused on the characteristics of the patient cohorts in the studies included in this review, the specific tasks in the context of the COVID-19 pandemic that machine and deep learning methods were used for, and their practical limitations. The technical analysis focused on the details of specific MDL methods and their performance. RESULTS Analysis of the 48 selected studies revealed that the majority (∼54 %) of them examined the application of MDL methods for the prediction of survival/mortality-related patient outcomes, while a smaller fraction (∼13 %) of studies also examined applications to the prediction of patients' physiological outcomes and hospital resource utilization. 21 % of the studies examined the application of MDL methods to multiple clinical tasks. Machine and deep learning methods have been shown to be effective at predicting several outcomes of COVID-19 patients, such as disease severity, complications, intensive care unit (ICU) transfer, and mortality. MDL methods also achieved high accuracy in predicting the required number of ICU beds and ventilators. CONCLUSION Machine and deep learning methods have been shown to be valuable tools for predicting disease severity, organ dysfunction and failure, patient outcomes, and hospital resource utilization during the COVID-19 pandemic. The discovered knowledge and our conclusions and recommendations can also be useful to healthcare professionals and artificial intelligence researchers in managing future pandemics.
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, School of Medicine, Nazarbayev University, Astana, Kazakhstan; Department of Anesthesiology, Intensive Care, and Pain Medicine, National Research Oncology Center, Astana, Kazakhstan.
| | - Alexander Kotov
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, USA.
| | - Maxim Popov
- Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
| | - Yerkin Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
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Baygül Eden A, Bakir Kayi A, Erdem MG, Demirci M. COVID-19 studies involving machine learning methods: A bibliometric study. Medicine (Baltimore) 2023; 102:e35564. [PMID: 37904407 PMCID: PMC10615482 DOI: 10.1097/md.0000000000035564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 09/18/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Machine learning (ML) and artificial intelligence (AI) techniques are gaining popularity as effective tools for coronavirus disease of 2019 (COVID-19) research. These strategies can be used in diagnosis, prognosis, therapy, and public health management. Bibliometric analysis quantifies the quality and impact of scholarly publications. ML in COVID-19 research is the focus of this bibliometric analysis. METHODS A comprehensive literature study found ML-based COVID-19 research. Web of Science (WoS) was used for the study. The searches included "machine learning," "artificial intelligence," and COVID-19. To find all relevant studies, 2 reviewers searched independently. The network visualization was analyzed using VOSviewer 1.6.19. RESULTS In the WoS Core, the average citation count was 13.6 ± 41.3. The main research areas were computer science, engineering, and science and technology. According to document count, Tao Huang wrote 14 studies, Fadi Al-Turjman wrote 11, and Imran Ashraf wrote 11. The US, China, and India produced the most studies and citations. The most prolific research institutions were Harvard Medical School, Huazhong University of Science and Technology, and King Abdulaziz University. In contrast, Nankai University, Oxford, and Imperial College London were the most mentioned organizations, reflecting their significant research contributions. First, "Covid-19" appeared 1983 times, followed by "machine learning" and "deep learning." The US Department of Health and Human Services funded this topic most heavily. Huang Tao, Feng Kaiyan, and Ashraf Imran pioneered bibliographic coupling. CONCLUSION This study provides useful insights for academics and clinicians studying COVID-19 using ML. Through bibliometric data analysis, scholars can learn about highly recognized and productive authors and countries, as well as the publications with the most citations and keywords. New data and methodologies from the pandemic are expected to advance ML and AI modeling. It is crucial to recognize that these studies will pioneer this subject.
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Affiliation(s)
- Arzu Baygül Eden
- Koç University, School of Medicine, Department of Biostatistics, Istanbul, Turkey
| | - Alev Bakir Kayi
- Istanbul University, Institute of Child Health, Department of Social Pediatrics, Istanbul, Türkiye
| | - Mustafa Genco Erdem
- Department of Internal Medicine, Faculty of Medicine, Beykent University, Istanbul, Turkey
| | - Mehmet Demirci
- Department of Medical Microbiology, Faculty of Medicine, Kirklareli University, Kirklareli, Turkey
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Pisano F, Cannas B, Fanni A, Pasella M, Canetto B, Giglio SR, Mocci S, Chessa L, Perra A, Littera R. Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19. Front Med (Lausanne) 2023; 10:1230733. [PMID: 37601789 PMCID: PMC10433226 DOI: 10.3389/fmed.2023.1230733] [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: 05/29/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. Methods This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. Results The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Discussion The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration.
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Affiliation(s)
- Fabio Pisano
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Barbara Cannas
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Alessandra Fanni
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Manuela Pasella
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | | | - Sabrina Rita Giglio
- Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Medical Genetics, R. Binaghi Hospital, Local Public Health and Social Care Unit (ASSL) of Cagliari, Cagliari, Italy
- Centre for Research University Services (CeSAR, Centro Servizi di Ateneo per la Ricerca), University of Cagliari, Cagliari, Monserrato, Italy
| | - Stefano Mocci
- Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Centre for Research University Services (CeSAR, Centro Servizi di Ateneo per la Ricerca), University of Cagliari, Cagliari, Monserrato, Italy
| | - Luchino Chessa
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Liver Unit, Department of Internal Medicine, University Hospital of Cagliari, Cagliari, Italy
| | - Andrea Perra
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Unit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Roberto Littera
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Medical Genetics, R. Binaghi Hospital, Local Public Health and Social Care Unit (ASSL) of Cagliari, Cagliari, Italy
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