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Ramón A, Bas A, Herrero S, Blasco P, Suárez M, Mateo J. Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach. J Clin Med 2024; 13:1837. [PMID: 38610602 PMCID: PMC11013017 DOI: 10.3390/jcm13071837] [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: 02/21/2024] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Background: Despite advancements in vaccination, early treatments, and understanding of SARS-CoV-2, its impact remains significant worldwide. Many patients require intensive care due to severe COVID-19. Remdesivir, a key treatment option among viral RNA polymerase inhibitors, lacks comprehensive studies on factors associated with its effectiveness. Methods: We conducted a retrospective study in 2022, analyzing data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared to predict factors influencing remdesivir's clinical benefits regarding mortality and hospital stay. Results: The extreme gradient boost (XGB) method showed the highest accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes in terms of mortality included limitations in life support, ventilatory support needs, lymphopenia, low albumin and hemoglobin levels, flu and/or coinfection, and cough. For hospital stay, factors included vaccine doses, lung density, pulmonary radiological status, comorbidities, oxygen therapy, troponin, lactate dehydrogenase levels, and asthenia. Conclusions: These findings underscore XGB's effectiveness in accurately categorizing COVID-19 patients undergoing remdesivir treatment.
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Affiliation(s)
- Antonio Ramón
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Andrés Bas
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
| | - Santiago Herrero
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
| | - Pilar Blasco
- Department of Pharmacy, University General Hospital, 46014 Valencia, Spain; (A.R.); (A.B.); (S.H.); (P.B.)
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Miguel Suárez
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Department of Gastroenterology, Virgen de la Luz Hospital, 16002 Cuenca, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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Casillas N, Ramón A, Torres AM, Blasco P, Mateo J. Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves. Viruses 2023; 15:2184. [PMID: 38005862 PMCID: PMC10675561 DOI: 10.3390/v15112184] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/21/2023] [Accepted: 10/28/2023] [Indexed: 11/26/2023] Open
Abstract
The impact of SARS-CoV-2 infection remains substantial on a global scale, despite widespread vaccination efforts, early therapeutic interventions, and an enhanced understanding of the disease's underlying mechanisms. At the same time, a significant number of patients continue to develop severe COVID-19, necessitating admission to intensive care units (ICUs). This study aimed to provide evidence concerning the most influential predictors of mortality among critically ill patients with severe COVID-19, employing machine learning (ML) techniques. To accomplish this, we conducted a retrospective multicenter investigation involving 684 patients with severe COVID-19, spanning from 1 June 2020 to 31 March 2023, wherein we scrutinized sociodemographic, clinical, and analytical data. These data were extracted from electronic health records. Out of the six supervised ML methods scrutinized, the extreme gradient boosting (XGB) method exhibited the highest balanced accuracy at 96.61%. The variables that exerted the greatest influence on mortality prediction encompassed ferritin, fibrinogen, D-dimer, platelet count, C-reactive protein (CRP), prothrombin time (PT), invasive mechanical ventilation (IMV), PaFi (PaO2/FiO2), lactate dehydrogenase (LDH), lymphocyte levels, activated partial thromboplastin time (aPTT), body mass index (BMI), creatinine, and age. These findings underscore XGB as a robust candidate for accurately classifying patients with COVID-19.
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Affiliation(s)
- Nazaret Casillas
- Department of Internal Medicine, Hospital Virgen De La Luz, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
| | - Antonio Ramón
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, University of Castilla-La Mancha, 16002 Cuenca, Spain
- Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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3
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Albagmi FM, Hussain M, Kamal K, Sheikh MF, AlNujaidi HY, Bah S, Althumiri NA, BinDhim NF. Predicting Multimorbidity Using Saudi Health Indicators (Sharik) Nationwide Data: Statistical and Machine Learning Approach. Healthcare (Basel) 2023; 11:2176. [PMID: 37570417 PMCID: PMC10418949 DOI: 10.3390/healthcare11152176] [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: 06/12/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 08/13/2023] Open
Abstract
The Saudi population is at high risk of multimorbidity. The risk of these morbidities can be reduced by identifying common modifiable behavioural risk factors. This study uses statistical and machine learning methods to predict factors for multimorbidity in the Saudi population. Data from 23,098 Saudi residents were extracted from the "Sharik" Health Indicators Surveillance System 2021. Participants were asked about their demographics and health indicators. Binary logistic models were used to determine predictors of multimorbidity. A backpropagation neural network model was further run using the predictors from the logistic regression model. Accuracy measures were checked using training, validation, and testing data. Females and smokers had the highest likelihood of experiencing multimorbidity. Age and fruit consumption also played a significant role in predicting multimorbidity. Regarding model accuracy, both logistic regression and backpropagation algorithms yielded comparable outcomes. The backpropagation method (accuracy 80.7%) was more accurate than the logistic regression model (77%). Machine learning algorithms can be used to predict multimorbidity among adults, particularly in the Middle East region. Different testing methods later validated the common predicting factors identified in this study. These factors are helpful and can be translated by policymakers to consider improvements in the public health domain.
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Affiliation(s)
- Faisal Mashel Albagmi
- College of Applied Medical Sciences, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia;
| | - Mehwish Hussain
- College of Public Health, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia; (H.Y.A.); (S.B.)
| | - Khurram Kamal
- Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan;
| | - Muhammad Fahad Sheikh
- Department of Mechanical Engineering, University of Management and Technology, Sialkot Campus, Lahore 54770, Pakistan;
| | - Heba Yaagoub AlNujaidi
- College of Public Health, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia; (H.Y.A.); (S.B.)
| | - Sulaiman Bah
- College of Public Health, Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia; (H.Y.A.); (S.B.)
| | - Nora A. Althumiri
- Sharik Association for Research and Studies, Abubaker Alsedeq, Riyadh 13326, Saudi Arabia; (N.A.A.); (N.F.B.)
| | - Nasser F. BinDhim
- Sharik Association for Research and Studies, Abubaker Alsedeq, Riyadh 13326, Saudi Arabia; (N.A.A.); (N.F.B.)
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4
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Giacconi R, Cardelli M, Piacenza F, Pierpaoli E, Farnocchia E, Di Rosa M, Bonfigli AR, Casoli T, Marchegiani F, Marcheselli F, Recchioni R, Stripoli P, Galeazzi R, Cherubini A, Fedecostante M, Sarzani R, Di Pentima C, Giordano P, Antonicelli R, Provinciali M, Lattanzio F. Effect of Cytomegalovirus Reactivation on Inflammatory Status and Mortality of Older COVID-19 Patients. Int J Mol Sci 2023; 24:ijms24076832. [PMID: 37047803 PMCID: PMC10094990 DOI: 10.3390/ijms24076832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/16/2023] [Accepted: 03/30/2023] [Indexed: 04/08/2023] Open
Abstract
Herpesviridae reactivation such as cytomegalovirus (CMV) has been described in severe COVID-19 (COronaVIrusDisease-2019). This study aimed to understand if CMV reactivation in older COVID-19 patients is associated with increased inflammation and in-hospital mortality. In an observational single-center cohort study, 156 geriatric COVID-19 patients were screened for CMV reactivation by RT-PCR. Participants underwent a comprehensive clinical investigation that included medical history, functional evaluation, laboratory tests and cytokine assays (TNF-α, IFN-α, IL-6, IL-10) at hospital admission. In 19 (12.2%) of 156 COVID-19 patients, CMV reactivation was detected. Multivariate Cox regression models showed that in-hospital mortality significantly increased among CMV positive patients younger than 87 years (HR: 9.94, 95% CI: 1.66–59.50). Other factors associated with in-hospital mortality were C-reactive protein (HR: 1.17, 95% CI: 1.05–1.30), neutrophil count (HR: 1.20, 95% CI: 1.01–1.42) and clinical frailty scale (HR:1.54, 95% CI: 1.04–2.28). In patients older than 87 years, neutrophil count (HR: 1.13, 95% CI: 1.05–1.21) and age (HR: 1.15, 95% CI: 1.01–1.31) were independently associated with in-hospital mortality. CMV reactivation was also correlated with increased IFN-α and TNF-α serum levels, but not with IL-6 and IL-10 serum changes. In conclusion, CMV reactivation was an independent risk factor for in-hospital mortality in COVID-19 patients younger than 87 years old, but not in nonagenarians.
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Affiliation(s)
- Robertina Giacconi
- Advanced Technology Center for Aging Research, IRCCS INRCA, Via Birarelli 8, 60121 Ancona, Italy
| | - Maurizio Cardelli
- Advanced Technology Center for Aging Research, IRCCS INRCA, Via Birarelli 8, 60121 Ancona, Italy
| | - Francesco Piacenza
- Advanced Technology Center for Aging Research, IRCCS INRCA, Via Birarelli 8, 60121 Ancona, Italy
| | - Elisa Pierpaoli
- Advanced Technology Center for Aging Research, IRCCS INRCA, Via Birarelli 8, 60121 Ancona, Italy
| | - Elisabetta Farnocchia
- Advanced Technology Center for Aging Research, IRCCS INRCA, Via Birarelli 8, 60121 Ancona, Italy
| | - MirKo Di Rosa
- Unit of Geriatric Pharmacoepidemiology and Biostatistics, IRCCS INRCA, 60124 Ancona, Italy
| | | | - Tiziana Casoli
- Center for Neurobiology of Aging, IRCCS INRCA, 60121 Ancona, Italy
| | - Francesca Marchegiani
- Center of Clinical Pathology and Innovative Therapy, IRCCS INRCA, 60121 Ancona, Italy
| | - Fiorella Marcheselli
- Center of Clinical Pathology and Innovative Therapy, IRCCS INRCA, 60121 Ancona, Italy
| | - Rina Recchioni
- Center of Clinical Pathology and Innovative Therapy, IRCCS INRCA, 60121 Ancona, Italy
| | - Pierpaolo Stripoli
- Center of Clinical Pathology and Innovative Therapy, IRCCS INRCA, 60121 Ancona, Italy
| | - Roberta Galeazzi
- Clinical Laboratory and Molecular Diagnostic, Italian National Research Center on Aging, IRCCS INRCA, 60127 Ancona, Italy
| | - Antonio Cherubini
- Geriatria, Accettazione Geriatrica e Centro di Ricerca per L’invecchiamento, IRCCS INRCA, 60127 Ancona, Italy
| | - Massimiliano Fedecostante
- Geriatria, Accettazione Geriatrica e Centro di Ricerca per L’invecchiamento, IRCCS INRCA, 60127 Ancona, Italy
| | - Riccardo Sarzani
- Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
- Internal Medicine and Geriatrics, Italian National Research Centre on Aging, Hospital “U. Sestilli”, IRCCS INRCA, 60127 Ancona, Italy
| | - Chiara Di Pentima
- Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
- Internal Medicine and Geriatrics, Italian National Research Centre on Aging, Hospital “U. Sestilli”, IRCCS INRCA, 60127 Ancona, Italy
| | - Piero Giordano
- Department of Clinical and Molecular Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
- Internal Medicine and Geriatrics, Italian National Research Centre on Aging, Hospital “U. Sestilli”, IRCCS INRCA, 60127 Ancona, Italy
| | | | - Mauro Provinciali
- Advanced Technology Center for Aging Research, IRCCS INRCA, Via Birarelli 8, 60121 Ancona, Italy
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Omit SBS, Akhter S, Rana HK, Rana ARMMH, Podder NK, Rakib MI, Nobi A. Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches. BIOMED RESEARCH INTERNATIONAL 2023; 2023:6996307. [PMID: 36685671 PMCID: PMC9848821 DOI: 10.1155/2023/6996307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/09/2022] [Accepted: 12/20/2022] [Indexed: 01/13/2023]
Abstract
Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.
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Affiliation(s)
- Shudeb Babu Sen Omit
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Salma Akhter
- Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Humayan Kabir Rana
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka 1207, Bangladesh
| | - A. R. M. Mahamudul Hasan Rana
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Nitun Kumar Podder
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
| | - Mahmudul Islam Rakib
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Ashadun Nobi
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
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Rojas-García M, Vázquez B, Torres-Poveda K, Madrid-Marina V. Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach. BMC Infect Dis 2023; 23:18. [PMID: 36631853 PMCID: PMC9832420 DOI: 10.1186/s12879-022-07951-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/19/2022] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Mexico ranks fifth worldwide in the number of deaths due to COVID-19. Identifying risk markers through easily accessible clinical data could help in the initial triage of COVID-19 patients and anticipate a fatal outcome, especially in the most socioeconomically disadvantaged regions. This study aims to identify markers that increase lethality risk in patients diagnosed with COVID-19, based on machine learning (ML) methods. Markers were differentiated by sex and age-group. METHODS A total of 11,564 cases of COVID-19 in Mexico were extracted from the Epidemiological Surveillance System for Viral Respiratory Disease. Four ML classification methods were trained to predict lethality, and an interpretability approach was used to identify those markers. RESULTS Models based on Extreme Gradient Boosting (XGBoost) yielded the best performance in a test set. This model achieved a sensitivity of 0.91, a specificity of 0.69, a positive predictive value of 0.344, and a negative predictive value of 0.965. For female patients, the leading markers are diabetes and arthralgia. For males, the main markers are chronic kidney disease (CKD) and chest pain. Dyspnea, hypertension, and polypnea increased the risk of death in both sexes. CONCLUSIONS ML-based models using an interpretability approach successfully identified risk markers for lethality by sex and age. Our results indicate that age is the strongest demographic factor for a fatal outcome, while all other markers were consistent with previous clinical trials conducted in a Mexican population. The markers identified here could be used as an initial triage, especially in geographic areas with limited resources.
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Affiliation(s)
- Mariano Rojas-García
- Center for Research on Infectious Diseases, Instituto Nacional de Salud Pública, Cuernavaca, 62100, Mexico
| | - Blanca Vázquez
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico
| | - Kirvis Torres-Poveda
- CONACyT-Instituto Nacional de Salud Pública, Av. Universidad 655, Santa María Ahuacatitlán, 62100, Cuernavaca, Mexico.
| | - Vicente Madrid-Marina
- Center for Research on Infectious Diseases, Instituto Nacional de Salud Pública, Cuernavaca, 62100, Mexico.
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Amiri P, Montazeri M, Ghasemian F, Asadi F, Niksaz S, Sarafzadeh F, Khajouei R. Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms. Digit Health 2023; 9:20552076231170493. [PMID: 37312960 PMCID: PMC10259141 DOI: 10.1177/20552076231170493] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/31/2023] [Indexed: 06/15/2023] Open
Abstract
Background The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Objective The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms. Methods This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting. Results This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath. Conclusion The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions.
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Affiliation(s)
- Parastoo Amiri
- Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran
| | - Mahdieh Montazeri
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Fahimeh Ghasemian
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fatemeh Asadi
- Student Research Committee, School of Management and Medical Information, Kerman University of Medical Sciences, Kerman, Iran
| | - Saeed Niksaz
- Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Farhad Sarafzadeh
- Infectious and Internal Medicine Department, Afzalipour Hospital, Kerman University of Medical Sciences, Kerman, Iran
| | - Reza Khajouei
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
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8
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Network-Based Data Analysis Reveals Ion Channel-Related Gene Features in COVID-19: A Bioinformatic Approach. Biochem Genet 2022; 61:471-505. [PMID: 36104591 PMCID: PMC9473477 DOI: 10.1007/s10528-022-10280-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 09/01/2022] [Indexed: 11/02/2022]
Abstract
Coronavirus disease 2019 (COVID-19) seriously threatens human health and has been disseminated worldwide. Although there are several treatments for COVID-19, its control is currently suboptimal. Therefore, the development of novel strategies to treat COVID-19 is necessary. Ion channels are located on the membranes of all excitable cells and many intracellular organelles and are key components involved in various biological processes. They are a target of interest when searching for drug targets. This study aimed to reveal the relevant molecular features of ion channel genes in COVID-19 based on bioinformatic analyses. The RNA-sequencing data of patients with COVID-19 and healthy subjects (GSE152418 and GSE171110 datasets) were obtained from the Gene Expression Omnibus (GEO) database. Ion channel genes were selected from the Hugo Gene Nomenclature Committee (HGNC) database. The RStudio software was used to process the data based on the corresponding R language package to identify ion channel-associated differentially expressed genes (DEGs). Based on the DEGs, Gene Ontology (GO) functional and pathway enrichment analyses were performed using the Enrichr web tool. The STRING database was used to generate a protein-protein interaction (PPI) network, and the Cytoscape software was used to screen for hub genes in the PPI network based on the cytoHubba plug-in. Transcription factors (TF)-DEG, DEG-microRNA (miRNA) and DEG-disease association networks were constructed using the NetworkAnalyst web tool. Finally, the screened hub genes as drug targets were subjected to enrichment analysis based on the DSigDB using the Enrichr web tool to identify potential therapeutic agents for COVID-19. A total of 29 ion channel-associated DEGs were identified. GO functional analysis showed that the DEGs were integral components of the plasma membrane and were mainly involved in inorganic cation transmembrane transport and ion channel activity functions. Pathway analysis showed that the DEGs were mainly involved in nicotine addiction, calcium regulation in the cardiac cell and neuronal system pathways. The top 10 hub genes screened based on the PPI network included KCNA2, KCNJ4, CACNA1A, CACNA1E, NALCN, KCNA5, CACNA2D1, TRPC1, TRPM3 and KCNN3. The TF-DEG and DEG-miRNA networks revealed significant TFs (FOXC1, GATA2, HINFP, USF2, JUN and NFKB1) and miRNAs (hsa-mir-146a-5p, hsa-mir-27a-3p, hsa-mir-335-5p, hsa-let-7b-5p and hsa-mir-129-2-3p). Gene-disease association network analysis revealed that the DEGs were closely associated with intellectual disability and cerebellar ataxia. Drug-target enrichment analysis showed that the relevant drugs targeting the hub genes CACNA2D1, CACNA1A, CACNA1E, KCNA2 and KCNA5 were gabapentin, gabapentin enacarbil, pregabalin, guanidine hydrochloride and 4-aminopyridine. The results of this study provide a valuable basis for exploring the mechanisms of ion channel genes in COVID-19 and clues for developing therapeutic strategies for COVID-19.
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De Freitas VM, Chiloff DM, Bosso GG, Teixeira JOP, Hernandes ICDG, Padilha MDP, Moura GC, De Andrade LGM, Mancuso F, Finamor FE, Serodio AMDB, Arakaki JSO, Sartori MGF, Ferreira PRA, Rangel ÉB. A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic. J Clin Med 2022; 11:jcm11154574. [PMID: 35956189 PMCID: PMC9369854 DOI: 10.3390/jcm11154574] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/30/2022] [Accepted: 08/01/2022] [Indexed: 12/28/2022] Open
Abstract
A machine learning approach is a useful tool for risk-stratifying patients with respiratory symptoms during the COVID-19 pandemic, as it is still evolving. We aimed to verify the predictive capacity of a gradient boosting decision trees (XGboost) algorithm to select the most important predictors including clinical and demographic parameters in patients who sought medical support due to respiratory signs and symptoms (RAPID RISK COVID-19). A total of 7336 patients were enrolled in the study, including 6596 patients that did not require hospitalization and 740 that required hospitalization. We identified that patients with respiratory signs and symptoms, in particular, lower oxyhemoglobin saturation by pulse oximetry (SpO2) and higher respiratory rate, fever, higher heart rate, and lower levels of blood pressure, associated with age, male sex, and the underlying conditions of diabetes mellitus and hypertension, required hospitalization more often. The predictive model yielded a ROC curve with an area under the curve (AUC) of 0.9181 (95% CI, 0.9001 to 0.9361). In conclusion, our model had a high discriminatory value which enabled the identification of a clinical and demographic profile predictive, preventive, and personalized of COVID-19 severity symptoms.
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Affiliation(s)
- Victor Muniz De Freitas
- Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Daniela Mendes Chiloff
- Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Giulia Gabriella Bosso
- Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | | | | | - Maira do Patrocínio Padilha
- Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Giovanna Corrêa Moura
- Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | | | - Frederico Mancuso
- Discipline of Emergency Medicine, Department of Medicine, Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Francisco Estivallet Finamor
- Discipline of Emergency Medicine, Department of Medicine, Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Aluísio Marçal de Barros Serodio
- Sector of Bioethics, Department of Surgery, Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Jaquelina Sonoe Ota Arakaki
- Pneumology Division, Department of Medicine, Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Marair Gracio Ferreira Sartori
- Department of Obstetrics, Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Paulo Roberto Abrão Ferreira
- Infectious Disease Division, Department of Medicine, Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
| | - Érika Bevilaqua Rangel
- Nephrology Division, Department of Medicine, Paulista School of Medicine, Hospital São Paulo, Federal University of São Paulo, São Paulo 04038-901, Brazil
- Correspondence:
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10
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Elshennawy NM, Ibrahim DM, Sarhan AM, Arafa M. Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19. Diagnostics (Basel) 2022; 12:1847. [PMID: 36010198 PMCID: PMC9406405 DOI: 10.3390/diagnostics12081847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 11/16/2022] Open
Abstract
The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients' mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a convolutional neural network (CNN); it is trained using a clinical dataset of 12,020 patients and is based on the 10-fold cross-validation (CV) approach for training and validation. The second predictive model, which we refer to as CV-LSTM + CNN, is developed by combining the long short-term memory (LSTM) approach with a CNN model. It is also trained using the clinical dataset based on the 10-fold CV approach for training and validation. The first two predictive models use the clinical dataset in its original CSV form. The last one, which we refer to as IMG-CNN, is a CNN model and is trained alternatively using the converted images of the clinical dataset, where each image corresponds to a data row from the original clinical dataset. The experimental results revealed that the IMG-CNN predictive model outperforms the other two with an average accuracy of 94.14%, a precision of 100%, a recall of 91.0%, a specificity of 100%, an F1-score of 95.3%, an AUC of 93.6%, and a loss of 0.22.
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Affiliation(s)
- Nada M. Elshennawy
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
| | - Dina M. Ibrahim
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Amany M. Sarhan
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
| | - Mohamed Arafa
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
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11
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Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features. Tomography 2022; 8:1791-1803. [PMID: 35894016 PMCID: PMC9326627 DOI: 10.3390/tomography8040151] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 11/17/2022] Open
Abstract
The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19 patients using clinical and chest radiographic data. Modeling was performed with a de-identified dataset of encounters prior to widespread vaccine availability. Non-imaging predictors included demographics, pre-admission clinical history, and past medical history variables. Imaging features were extracted from chest radiographs by applying a deep convolutional neural network with transfer learning. A multi-layer perceptron combining 64 deep learning features from chest radiographs with 98 patient clinical features was trained to predict mortality. The Local Interpretable Model-Agnostic Explanations (LIME) method was used to explain model predictions. Non-imaging data alone predicted mortality with an ROC-AUC of 0.87 ± 0.03 (mean ± SD), while the addition of imaging data improved prediction slightly (ROC-AUC: 0.91 ± 0.02). The application of LIME to the combined imaging and clinical model found HbA1c values to contribute the most to model prediction (17.1 ± 1.7%), while imaging contributed 8.8 ± 2.8%. Age, gender, and BMI contributed 8.7%, 8.2%, and 7.1%, respectively. Our findings demonstrate a viable explainable AI approach to quantify the contributions of imaging and clinical data to COVID mortality predictions.
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12
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Fang L, Liang X. ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis. Comput Biol Med 2022; 146:105615. [PMID: 35605484 PMCID: PMC9112616 DOI: 10.1016/j.compbiomed.2022.105615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 12/16/2022]
Abstract
The novel coronavirus disease 2019 (COVID-19) pandemic has severely impacted the world. The early diagnosis of COVID-19 and self-isolation can help curb the spread of the virus. Besides, a simple and accurate diagnostic method can help in making rapid decisions for the treatment and isolation of patients. The analysis of patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes will be performed in the model. In this paper, a symptom-based machine learning (ML) model with a new learning mechanism called Intensive Symptom Weight Learning Mechanism (ISW-LM) is proposed. The proposed model designs three new symptoms' weight functions to identify the most relevant symptoms used to diagnose and classify COVID-19. To verify the efficiency of the proposed model, multiple laboratory and clinical datasets containing epidemiological symptoms and blood tests are used. Experiments indicate that the importance of COVID-19 infection symptoms varies between countries and regions. In most datasets, the most frequent and significant predictive symptoms for diagnosing COVID-19 are fever, sore throat, and cough. The experiment also compares the state-of-the-art methods with the proposed method, which shows that the proposed model has a high accuracy rate of up to 97.1711%. The positive results indicate that the proposed learning mechanism can help clinicians quickly diagnose and screen patients for COVID-19 at an early stage.
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13
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Krenn K, Tretter V, Kraft F, Ullrich R. The Renin-Angiotensin System as a Component of Biotrauma in Acute Respiratory Distress Syndrome. Front Physiol 2022; 12:806062. [PMID: 35498160 PMCID: PMC9043684 DOI: 10.3389/fphys.2021.806062] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 12/29/2021] [Indexed: 12/13/2022] Open
Abstract
Acute respiratory distress syndrome (ARDS) is a major concern in critical care medicine with a high mortality of over 30%. Injury to the lungs is caused not only by underlying pathological conditions such as pneumonia, sepsis, or trauma, but also by ventilator-induced lung injury (VILI) resulting from high positive pressure levels and a high inspiratory oxygen fraction. Apart from mechanical factors that stress the lungs with a specific physical power and cause volutrauma and barotrauma, it is increasingly recognized that lung injury is further aggravated by biological mediators. The COVID-19 pandemic has led to increased interest in the role of the renin-angiotensin system (RAS) in the context of ARDS, as the RAS enzyme angiotensin-converting enzyme 2 serves as the primary cell entry receptor for severe acute respiratory syndrome (SARS) coronavirus (CoV)-2. Even before this pandemic, studies have documented the involvement of the RAS in VILI and its dysregulation in clinical ARDS. In recent years, analytical tools for RAS investigation have made major advances based on the optimized precision and detail of mass spectrometry. Given that many clinical trials with pharmacological interventions in ARDS were negative, RAS-modifying drugs may represent an interesting starting point for novel therapeutic approaches. Results from animal models have highlighted the potential of RAS-modifying drugs to prevent VILI or treat ARDS. While these drugs have beneficial pulmonary effects, the best targets and application forms for intervention still have to be determined to avoid negative effects on the circulation in clinical settings.
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14
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Jamshidi E, Asgary A, Tavakoli N, Zali A, Setareh S, Esmaily H, Jamaldini SH, Daaee A, Babajani A, Sendani Kashi MA, Jamshidi M, Jamal Rahi S, Mansouri N. Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU. Front Digit Health 2022; 3:681608. [PMID: 35098205 PMCID: PMC8792458 DOI: 10.3389/fdgth.2021.681608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 12/22/2021] [Indexed: 01/28/2023] Open
Abstract
Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset. Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.
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Affiliation(s)
- Elham Jamshidi
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amirhossein Asgary
- Department of Biotechnology, College of Sciences, University of Tehran, Tehran, Iran
| | - Nader Tavakoli
- Trauma and Injury Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Alireza Zali
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soroush Setareh
- Department of Biotechnology, College of Sciences, University of Tehran, Tehran, Iran
| | - Hadi Esmaily
- Department of Clinical Pharmacy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Hamid Jamaldini
- Department of Genetic, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Amir Daaee
- School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
| | - Amirhesam Babajani
- Department of Pharmacology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Masoud Jamshidi
- Department of Exercise Physiology, Tehran University, Tehran, Iran
| | - Sahand Jamal Rahi
- Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Nahal Mansouri
- Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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15
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Ahamed KU, Islam M, Uddin A, Akhter A, Paul BK, Yousuf MA, Uddin S, Quinn JM, Moni MA. A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images. Comput Biol Med 2021; 139:105014. [PMID: 34781234 PMCID: PMC8566098 DOI: 10.1016/j.compbiomed.2021.105014] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 12/16/2022]
Abstract
Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.
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Affiliation(s)
- Khabir Uddin Ahamed
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Manowarul Islam
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh,Corresponding author
| | - Ashraf Uddin
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Arnisha Akhter
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Bikash Kumar Paul
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Mohammad Abu Yousuf
- Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh
| | - Shahadat Uddin
- Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Darlington, NSW, 2008, Australia
| | - Julian M.W. Quinn
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
| | - Mohammad Ali Moni
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia,Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia,Corresponding author. Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
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16
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Chowdhury UN, Faruqe MO, Mehedy M, Ahmad S, Islam MB, Shoombuatong W, Azad A, Moni MA. Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection. Comput Biol Med 2021; 138:104891. [PMID: 34624759 PMCID: PMC8479467 DOI: 10.1016/j.compbiomed.2021.104891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/21/2021] [Accepted: 09/21/2021] [Indexed: 12/16/2022]
Abstract
The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.
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Affiliation(s)
- Utpala Nanda Chowdhury
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Omar Faruqe
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Mehedy
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Shamim Ahmad
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - M. Babul Islam
- Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - A.K.M. Azad
- Faculty of Science, Engineering & Technology, Swinburne University of Technology Sydney, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD 4072, Australia,Corresponding author
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