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Kalezhi J, Chibuluma M, Chembe C, Chama V, Lungo F, Kunda D. Modelling Covid-19 infections in Zambia using data mining techniques. RESULTS IN ENGINEERING 2022; 13:100363. [PMID: 35317385 PMCID: PMC8813672 DOI: 10.1016/j.rineng.2022.100363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/08/2022] [Accepted: 02/01/2022] [Indexed: 06/14/2023]
Abstract
The outbreak of Covid-19 pandemic has been declared a global health crisis by the World Health Organization since its emergence. Several researchers have proposed a number of techniques to understand how the pandemic affects the populations. Reported among these techniques are data mining models which have been successfully applied in a wide range of situations before the advent of Covid-19 pandemic. In this work, the researchers have applied a number of existing data mining methods (classifiers) available in the Waikato Environment for Knowledge Analysis (WEKA) machine learning library. WEKA was used to gain a better understanding on how the epidemic spread within Zambia. The classifiers used are J48 decision tree, Multilayer Perceptron and Naïve Bayes among others. The predictions of these techniques are compared against simpler classifiers and those reported in related works.
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Affiliation(s)
- Josephat Kalezhi
- Department of Computer Engineering, Copperbelt University, Kitwe, Zambia
| | - Mathews Chibuluma
- Department of Information Technology/Systems, Copperbelt University, Kitwe, Zambia
| | | | - Victoria Chama
- Department of Computer Science and Information Technology, Mulungushi University, Kabwe, Zambia
| | - Francis Lungo
- School of Social Sciences, Mulungushi University, Kabwe, Zambia
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Abstract
COVID-19 has provoked enormous negative impacts on human lives and the world economy. In order to help in the fight against this pandemic, this study evaluates different databases’ systems and selects the most suitable for storing, handling, and mining COVID-19 data. We evaluate different SQL and NoSQL database systems using the following metrics: query runtime, memory used, CPU used, and storage size. The databases systems assessed were Microsoft SQL Server, MongoDB, and Cassandra. We also evaluate Data Mining algorithms, including Decision Trees, Random Forest, Naive Bayes, and Logistic Regression using Orange Data Mining software data classification tests. Classification tests were performed using cross-validation in a table with about 3 M records, including COVID-19 exams with patients’ symptoms. The Random Forest algorithm has obtained the best average accuracy, recall, precision, and F1 Score in the COVID-19 predictive model performed in the mining stage. In performance evaluation, MongoDB has presented the best results for almost all tests with a large data volume.
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53
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Zhang H, Zhong F, Wang B, Liao M. A nomogram predicting the severity of COVID-19 based on initial clinical and radiologic characteristics. Future Virol 2022. [PMID: 35371273 PMCID: PMC8862443 DOI: 10.2217/fvl-2020-0193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 01/24/2022] [Indexed: 12/15/2022]
Abstract
Aim: This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. Patients & methods: From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and multivariate logistic regression were used to identify variables for the nomogram. Results: In total, 104 patients were included. Based on statistical analysis, age, levels of neutrophil count, creatinine, procalcitonin and numbers of involved lung segments were identified for nomogram. The area under the curve was 0.939 (95% CI: 0.893–0.984). The calibration curve showed good agreement between prediction of nomogram and observation in the primary cohort. Conclusion: An easy-to-use nomogram with great discrimination was built to predict the severity of COVID-19.
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Affiliation(s)
- Hanfei Zhang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Feiyang Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Binchen Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Meiyan Liao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
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54
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Different Scales of Medical Data Classification Based on Machine Learning Techniques: A Comparative Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In recent years, medical data have vastly increased due to the continuous generation of digital data. The different forms of medical data, such as reports, textual, numerical, monitoring, and laboratory data generate the so-called medical big data. This paper aims to find the best algorithm which predicts new medical data with high accuracy, since good prediction accuracy is essential in medical fields. To achieve the study’s goal, the best accuracy algorithm and least processing time algorithm are defined through an experiment and comparison of seven different algorithms, including Naïve bayes, linear model, regression, decision tree, random forest, gradient boosted tree, and J48. The conducted experiments have allowed the prediction of new medical big data that reach the algorithm with the best accuracy and processing time. Here, we find that the best accuracy classification algorithm is the random forest with accuracy values of 97.58%, 83.59%, and 90% for heart disease, M-health, and diabetes datasets, respectively. The Naïve bayes has the lowest processing time with values of 0.078, 7.683, and 22.374 s for heart disease, M-health, and diabetes datasets, respectively. In addition, the best result of the experiment is obtained by the combination of the CFS feature selection algorithm with the Random Forest classification algorithm. The results of applying RF with the combination of CFS on the heart disease dataset are as follows: Accuracy of 90%, precision of 83.3%, sensitivity of 100, and consuming time of 3 s. Moreover, the results of applying this combination on the M-health dataset are as follows: Accuracy of 83.59%, precision of 74.3%, sensitivity of 93.1, and consuming time of 13.481 s. Furthermore, the results on the diabetes dataset are as follows: Accuracy of 97.58%, precision of 86.39%, sensitivity of 97.14, and consuming time of 56.508 s.
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A. Elzeheiry H, Barakat S, Rezk A. An Efficient Ensemble Model for Various Scale Medical Data. COMPUTERS, MATERIALS & CONTINUA 2022; 73:1283-1305. [DOI: 10.32604/cmc.2022.027345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 04/12/2022] [Indexed: 09/01/2023]
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56
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Khan MS, Yousafi Q, Bibi S, Azhar M, Ihsan A. Bioinformatics-Based Approaches to Study Virus-Host Interactions During SARS-CoV-2 Infection. Methods Mol Biol 2022; 2452:197-212. [PMID: 35554909 DOI: 10.1007/978-1-0716-2111-0_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
As the knowledge of biomolecules is increasing from the last decades, it is helping the researchers to understand the unsolved issues regarding virology. Recent technologies in high-throughput sequencing are providing the swift generation of SARS-CoV-2 genomic data with the basic inside of viral infection. Owing to various virus-host protein interactions, high-throughput technologies are unable to provide complete details of viral pathogenesis. Identifying the virus-host protein interactions using bioinformatics approaches can assist in understanding the mechanism of SARS-CoV-2 infection and pathogenesis. In this chapter, recent integrative bioinformatics approaches are discussed to help the virologists and computational biologists in the identification of structurally similar proteins of human and SARS-CoV-2 virus, and to predict the potential of virus-host interactions. Considering experimental and time limitations for effective viral drug development, computational aided drug design (CADD) can reduce the gap between drug prediction and development. More research with respect to evolutionary solutions could be helpful to make a new pipeline for virus-host protein-protein interactions and provide more understanding to disclose the cases of host switch, and also expand the virulence of the pathogen and host range in developing viral infections.
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Affiliation(s)
- Muhammad Saad Khan
- Department of Biosciences, COMSATS University Islamabad, Sahiwal, Pakistan
| | - Qudsia Yousafi
- Department of Biosciences, COMSATS University Islamabad, Sahiwal, Pakistan
| | - Shabana Bibi
- Yunnan Herbal Laboratory, School of Ecology and Environmental Sciences, Yunnan University, Kunming, Yunnan, China
| | - Muhammad Azhar
- Department of Biosciences, COMSATS University Islamabad, Sahiwal, Pakistan
| | - Awais Ihsan
- Department of Biosciences, COMSATS University Islamabad, Sahiwal, Pakistan.
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Saif S, Das P, Biswas S. A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast. JOURNAL OF THE INSTITUTION OF ENGINEERS (INDIA): SERIES B 2021. [PMCID: PMC7814866 DOI: 10.1007/s40031-021-00538-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Sohail Saif
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
| | - Priya Das
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
| | - Suparna Biswas
- Department of Computer Science & Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, WB India
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58
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Sarker S, Jamal L, Ahmed SF, Irtisam N. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. ROBOTICS AND AUTONOMOUS SYSTEMS 2021; 146:103902. [PMID: 34629751 PMCID: PMC8493645 DOI: 10.1016/j.robot.2021.103902] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/03/2021] [Accepted: 09/13/2021] [Indexed: 05/05/2023]
Abstract
The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.
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Affiliation(s)
- Sujan Sarker
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Lafifa Jamal
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Syeda Faiza Ahmed
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Niloy Irtisam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
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59
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Miao R, Dong X, Xie SL, Liang Y, Lo SL. UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients. BMC Med Imaging 2021; 21:174. [PMID: 34809589 PMCID: PMC8607405 DOI: 10.1186/s12880-021-00704-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 11/10/2021] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. METHODS This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. RESULTS The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models. CONCLUSION In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.
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Affiliation(s)
- Rui Miao
- Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
- Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Xin Dong
- Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Sheng-Li Xie
- Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, 510006, China
| | - Yong Liang
- Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
- Department of State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China
| | - Sio-Long Lo
- Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China.
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60
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Haas Q, Borisov N, Alvarez DV, Ferdowsi S, von Mayenn L, Teodoro D, Amini P. Vaccine Development in the Time of COVID-19: The Relevance of the Risklick AI to Assist in Risk Assessment and Optimize Performance. Front Digit Health 2021; 3:745674. [PMID: 34796360 PMCID: PMC8593331 DOI: 10.3389/fdgth.2021.745674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/24/2021] [Indexed: 01/18/2023] Open
Abstract
The 2019 coronavirus (COVID-19) pandemic revealed the urgent need for the acceleration of vaccine development worldwide. Rapid vaccine development poses numerous risks for each category of vaccine technology. By using the Risklick artificial intelligence (AI), we estimated the risks associated with all types of COVID-19 vaccine during the early phase of vaccine development. We then performed a postmortem analysis of the probability and the impact matrix calculations by comparing the 2020 prognosis to the contemporary situation. We used the Risklick AI to evaluate the risks and their incidence associated with vaccine development in the early stage of the COVID-19 pandemic. Our analysis revealed the diversity of risks among vaccine technologies currently used by pharmaceutical companies providing vaccines. This analysis highlighted the current and future potential pitfalls connected to vaccine production during the COVID-19 pandemic. Hence, the Risklick AI appears as an essential tool in vaccine development for the treatment of COVID-19 in order to formally anticipate the risks, and increases the overall performance from the production to the distribution of the vaccines. The Risklick AI could, therefore, be extended to other fields of research and development and represent a novel opportunity in the calculation of production-associated risks.
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Affiliation(s)
- Quentin Haas
- Risklick AG, Spin-off University of Bern, Bern, Switzerland.,Clinical Trial Unit Bern, University of Bern, Bern, Switzerland
| | - Nikolay Borisov
- Risklick AG, Spin-off University of Bern, Bern, Switzerland.,Clinical Trial Unit Bern, University of Bern, Bern, Switzerland
| | - David Vicente Alvarez
- HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Sohrab Ferdowsi
- HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Leonhard von Mayenn
- Risklick AG, Spin-off University of Bern, Bern, Switzerland.,Clinical Trial Unit Bern, University of Bern, Bern, Switzerland
| | - Douglas Teodoro
- HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland.,Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Poorya Amini
- Risklick AG, Spin-off University of Bern, Bern, Switzerland.,Clinical Trial Unit Bern, University of Bern, Bern, Switzerland
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61
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Aberi P, Arabzadeh R, Insam H, Markt R, Mayr M, Kreuzinger N, Rauch W. Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:10778. [PMID: 34682523 PMCID: PMC8535556 DOI: 10.3390/ijerph182010778] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/05/2021] [Accepted: 10/09/2021] [Indexed: 12/18/2022]
Abstract
Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.
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Affiliation(s)
- Parisa Aberi
- Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria; (P.A.); (R.A.)
| | - Rezgar Arabzadeh
- Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria; (P.A.); (R.A.)
| | - Heribert Insam
- Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria; (H.I.); (R.M.); (M.M.)
| | - Rudolf Markt
- Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria; (H.I.); (R.M.); (M.M.)
| | - Markus Mayr
- Department of Microbiology, University Innsbruck, 6020 Innsbruck, Austria; (H.I.); (R.M.); (M.M.)
| | - Norbert Kreuzinger
- Institute for Water Quality and Resource Management, Technology University Vienna, 1040 Vienna, Austria;
| | - Wolfgang Rauch
- Department of Infrastructure, University Innsbruck, 6020 Innsbruck, Austria; (P.A.); (R.A.)
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62
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Moulaei K, Ghasemian F, Bahaadinbeigy K, Ershad Sarbi R, Mohamadi Taghiabad Z. Predicting Mortality of COVID-19 Patients based on Data Mining Techniques. J Biomed Phys Eng 2021; 11:653-662. [PMID: 34722410 PMCID: PMC8546157 DOI: 10.31661/jbpe.v0i0.2104-1300] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/20/2021] [Indexed: 04/23/2023]
Abstract
If Coronavirus (COVID-19) is not predicted, managed, and controlled timely, the health systems of any country and their people will face serious problems. Predictive models can be helpful in health resource management and prevent outbreak and death caused by COVID-19. The present study aimed at predicting mortality in patients with COVID-19 based on data mining techniques. To do this study, the mortality factors of COVID-19 patients were first identified based on different studies. These factors were confirmed by specialist physicians. Based on the confirmed factors, the data of COVID-19 patients were extracted from 850 medical records. Decision tree (J48), MLP, KNN, random forest, and SVM data mining models were used for prediction. The models were evaluated based on accuracy, precision, specificity, sensitivity, and the ROC curve. According to the results, the most effective factor used to predict the death of COVID-19 patients was dyspnea. Based on ROC (1.000), accuracy (99.23%), precision (99.74%), sensitivity (98.25%) and specificity (99.84%), the random forest was the best model in predicting of mortality than other models. After the random forest, KNN5, MLP, and J48 models were ranked next, respectively. Data analysis of COVID-19 patients can be a suitable and practical tool for predicting the mortality of these patients. Given the sensitivity of medical science concerning maintaining human life and lack of specialized human resources in the health system, using the proposed models can increase the chances of successful treatment, prevent early death and reduce the costs associated with long treatments for patients, hospitals and the insurance industry.
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Affiliation(s)
- Khadijeh Moulaei
- PhD Candidate, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Fahimeh Ghasemian
- PhD, Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University Kerman, Kerman, Iran
| | - Kambiz Bahaadinbeigy
- MD, PhD, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Roghayeh Ershad Sarbi
- PhD, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Zahra Mohamadi Taghiabad
- MSc, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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63
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Al-Ali A, Elharrouss O, Qidwai U, Al-Maaddeed S. ANFIS-Net for automatic detection of COVID-19. Sci Rep 2021; 11:17318. [PMID: 34453082 PMCID: PMC8397755 DOI: 10.1038/s41598-021-96601-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 08/04/2021] [Indexed: 12/24/2022] Open
Abstract
Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.
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Affiliation(s)
- Afnan Al-Ali
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar.
| | - Omar Elharrouss
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
| | - Uvais Qidwai
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
| | - Somaya Al-Maaddeed
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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Wang J, Liu C, Li J, Yuan C, Zhang L, Jin C, Xu J, Wang Y, Wen Y, Lu H, Li B, Chen C, Li X, Shen D, Qian D, Wang J. iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients. NPJ Digit Med 2021; 4:124. [PMID: 34400751 PMCID: PMC8367981 DOI: 10.1038/s41746-021-00496-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 07/21/2021] [Indexed: 02/07/2023] Open
Abstract
Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.
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Affiliation(s)
- Jun Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jingwen Li
- Department of Gastroenterology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Cheng Yuan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lichi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng Jin
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jianwei Xu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yaqi Wang
- College of Media, Communication University of Zhejiang, Hangzhou, China
| | - Yaofeng Wen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hongbing Lu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiangdong Li
- Department of Radiology, General Hospital of Southern Theatre Command, PLA, Guangzhou, China.
- Department of Radiology, Huoshenshan Hospital, Wuhan, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd, Shanghai, China.
| | - Dahong Qian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai, China.
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
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Hou J, Gao T. Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection. Sci Rep 2021; 11:16071. [PMID: 34373554 PMCID: PMC8352869 DOI: 10.1038/s41598-021-95680-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 07/28/2021] [Indexed: 02/07/2023] Open
Abstract
To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.
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Affiliation(s)
- Jie Hou
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, Guangdong, China
| | - Terry Gao
- Counties Manukau District Health Board, Auckland, 1640, New Zealand.
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Pulgar-Sánchez M, Chamorro K, Fors M, Mora FX, Ramírez H, Fernandez-Moreira E, Ballaz SJ. Biomarkers of severe COVID-19 pneumonia on admission using data-mining powered by common laboratory blood tests-datasets. Comput Biol Med 2021; 136:104738. [PMID: 34391001 PMCID: PMC8349478 DOI: 10.1016/j.compbiomed.2021.104738] [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: 04/24/2021] [Revised: 07/14/2021] [Accepted: 08/02/2021] [Indexed: 12/23/2022]
Abstract
In the epidemiological COVID-19 research, artificial intelligence is a unique approach to make predictions about disease severity to manage COVID-19 patients. A limitation of artificial intelligence is, however, the high risk of bias. We investigated the skill of data mining and machine learning, two advanced forms of artificial intelligence, to predict severe COVID-19 pneumonia based on routine laboratory tests. A sample of 4009 COVID-19 patients was divided into Severe (PaO2< 60 mmHg, 489 cases) and Non-Severe (PaO2 ≥ 60 mmHg, 3520 cases) groups according to blood hypoxemia on admission and their laboratory datasets analyzed by the R software and WEKA workbench. After curation, data were processed for the selection of the most influential features including hemogram, pCO2, blood acid-base balance, prothrombin time, inflammation biomarkers, and glucose. The best fit of variables was successfully confirmed by either the Multilayer Perceptron, a feedforward neural network algorithm that performed machine recognition of severe COVID-19 with 96.5% precision, or by the C4.5 software, a supervised learning algorithm based on an objective-predefined variable (severity) that generated a decision tree with 89.4% precision. Finally, a complex bivariate Pearson's correlation matrix combined with advanced hierarchical clustering (dendrograms) were conducted for knowledge discovery. The hidden structure of the datasets revealed shift patterns related to the development of COVID-19-induced pneumonia that involved the lymphocyte-to-C-reactive protein and leukocyte-to-C-protein ratios, neutrophil %, pH and pCO2. The data mining approaches to the hematological fluctuations associated with severe COVID-19 pneumonia could not only anticipate adverse clinical outcomes, but also reveal putative therapeutic targets.
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Affiliation(s)
- Mary Pulgar-Sánchez
- Escuela de Ciencias Biológicas e Ingeniería. Universidad Yachay Tech, Urcuquí, Ecuador
| | - Kevin Chamorro
- Escuela de Matemáticas y Ciencias Computacionales. Universidad Yachay Tech, Urcuquí, Ecuador; Universidad Técnica Del Norte, Ibarra, Ecuador
| | - Martha Fors
- Escuela de Medicina; Universidad de las Américas, Quito, Ecuador
| | | | - Hégira Ramírez
- Escuela de Medicina; Universidad de las Américas, Quito, Ecuador
| | | | - Santiago J Ballaz
- Escuela de Ciencias Biológicas e Ingeniería. Universidad Yachay Tech, Urcuquí, Ecuador; Escuela de Medicina, Universidad Espíritu Santo, Samborondón, Ecuador.
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67
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Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R, Gorriz JM, Hussain S, Sani ZA, Moosaei H, Khosravi A, Nahavandi S, Islam SMS. Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep 2021; 11:15343. [PMID: 34321491 PMCID: PMC8319175 DOI: 10.1038/s41598-021-93543-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/25/2021] [Indexed: 02/07/2023] Open
Abstract
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
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Affiliation(s)
- Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Navid Hoseini Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | | | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia.
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam, 786004, India
| | | | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Bojnord, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, 3220, Australia
- Cardiovascular Division, The George Institute for Global Health, Newtown, Australia
- Sydney Medical School, University of Sydney, Camperdown, Australia
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68
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Rahman MM, Islam MM, Manik MMH, Islam MR, Al-Rakhami MS. Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic. SN COMPUTER SCIENCE 2021; 2:384. [PMID: 34308367 PMCID: PMC8287848 DOI: 10.1007/s42979-021-00774-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 07/11/2021] [Indexed: 12/24/2022]
Abstract
Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare facilities. The healthcare system is going through a critical time because of the COVID-19 pandemic. Modern technologies such as deep learning, machine learning, and data science are contributing to fight COVID-19. The paper aims to highlight the role of machine learning approaches in this pandemic situation. We searched for the latest literature regarding machine learning approaches for COVID-19 from various sources like IEEE Xplore, PubMed, Google Scholar, Research Gate, and Scopus. Then, we analyzed this literature and described them throughout the study. In this study, we noticed four different applications of machine learning methods to combat COVID-19. These applications are trying to contribute in various aspects like helping physicians to make confident decisions, policymakers to take fruitful decisions, and identifying potentially infected people. The major challenges of existing systems with possible future trends are outlined in this paper. The researchers are coming with various technologies using machine learning techniques to face the COVID-19 pandemic. These techniques are serving the healthcare system in a great deal. We recommend that machine learning can be a useful tool for proper analyzing, screening, tracking, forecasting, and predicting the characteristics and trends of COVID-19.
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Affiliation(s)
- Mohammad Marufur Rahman
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md. Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md. Motaleb Hossen Manik
- Department of Computer Science and Engineering, Khulna University of Engineering and Technology, Khulna, 9203 Bangladesh
| | - Md. Rabiul Islam
- Department of Electrical and Electronic Engineering, Bangladesh Army University of Engineering and Technology, Natore, 6431 Bangladesh
| | - Mabrook S. Al-Rakhami
- Research Chair of Pervasive and Mobile Computing, Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
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69
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Zhao W, Jiang W, Qiu X. Deep learning for COVID-19 detection based on CT images. Sci Rep 2021; 11:14353. [PMID: 34253822 PMCID: PMC8275612 DOI: 10.1038/s41598-021-93832-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/18/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has tremendously impacted patients and medical systems globally. Computed tomography images can effectively complement the reverse transcription-polymerase chain reaction testing. This study adopted a convolutional neural network for COVID-19 testing. We examined the performance of different pre-trained models on CT testing and identified that larger, out-of-field datasets boost the testing power of the models. This suggests that a priori knowledge of the models from out-of-field training is also applicable to CT images. The proposed transfer learning approach proves to be more successful than the current approaches described in literature. We believe that our approach has achieved the state-of-the-art performance in identification thus far. Based on experiments with randomly sampled training datasets, the results reveal a satisfactory performance by our model. We investigated the relevant visual characteristics of the CT images used by the model; these may assist clinical doctors in manual screening.
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Affiliation(s)
- Wentao Zhao
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
- School of Intelligent Transportation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, China
| | - Wei Jiang
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Xinguo Qiu
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
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70
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Machine Learning Predictive Models for Coronary Artery Disease. ACTA ACUST UNITED AC 2021; 2:350. [PMID: 34179828 PMCID: PMC8218284 DOI: 10.1007/s42979-021-00731-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 05/31/2021] [Indexed: 12/12/2022]
Abstract
Coronary artery disease (CAD) is the commonest type of heart disease and over 80% of the deaths resulted from the diseases occurred in developing countries including Nigeria, with majority being in those victims are below 70 years of age. Though, CAD is not a well known disease in Nigeria but however in year 2014, 2.82% of the total of deaths occurred in the country were due to the disease. In this study, a machine leaning predictive models for CAD has been developed with diagnostic CAD dataset obtained in the two General Hospitals in Kano State-Nigeria. The dataset applied on machine learning algorithms which include support vector machine, K nearest neighbor, random tree, Naïve Bayes, gradient boosting and logistic regression algorithms to build the predictive models and the models were evaluated based accuracy, specificity, sensitivity and receiver operating curve (ROC) performance evaluation techniques. In terms of accuracy random forest-based machine learning model emerged to be the best model with 92.04%, for specificity Naive Bayes based machine learning model emerged to be the best model with 92.40%, while for sensitivity support vector machine based machine learning model emerged to be the best model with 87.34% and for ROC, random forest-based machine learning model emerged to be the best model with 92.20%. The decision tree generated with random forest machine learning algorithm which happened to be best model in terms accuracy and ROC can be converted into production rules and be used develop expert system for diagnosis of CAD patients in Nigeria.
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71
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Surianarayanan C, Chelliah PR. Leveraging Artificial Intelligence (AI) Capabilities for COVID-19 Containment. NEW GENERATION COMPUTING 2021; 39:717-741. [PMID: 34131359 PMCID: PMC8191724 DOI: 10.1007/s00354-021-00128-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/05/2021] [Indexed: 05/15/2023]
Abstract
The Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered Severe Acute Respiratory Syndrome Coronavirus two (SARS-CoV-2). Most of the people do not have the acquired immunity to fight this virus. There is no specific treatment or medicine to cure the disease. The effects of this disease appear to vary from individual to individual, right from mild cough, fever to respiratory disease. It also leads to mortality in many people. As the virus has a very rapid transmission rate, the entire world is in distress. The control and prevention of this disease has evolved as an urgent and critical issue to be addressed through technological solutions. The Healthcare industry therefore needs support from the domain of artificial intelligence (AI). AI has the inherent capability of imitating the human brain and assisting in decision-making support by automatically learning from input data. It can process huge amounts of data quickly without getting tiresome and making errors. AI technologies and tools significantly relieve the burden of healthcare professionals. In this paper, we review the critical role of AI in responding to different research challenges around the COVID-19 crisis. A sample implementation of a powerful probabilistic machine learning (ML) algorithm for assessment of risk levels of individuals is incorporated in this paper. Other pertinent application areas such as surveillance of people and hotspots, mortality prediction, diagnosis, prognostic assistance, drug repurposing and discovery of protein structure, and vaccine are presented. The paper also describes various challenges that are associated with the implementation of AI-based tools and solutions for practical use.
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Affiliation(s)
- Chellammal Surianarayanan
- Government Arts and Science College (Formerly Bharathidasan University Constituent Arts and Science College), Affiliated to Bharathidasan University, Tiruchirappalli, Tamilnadu India
| | - Pethuru Raj Chelliah
- Site Reliability Engineering Division, Reliance Jio Platforms Ltd, Bangalore, India
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72
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Gomes JC, Masood AI, Silva LHDS, da Cruz Ferreira JRB, Freire Júnior AA, Rocha ALDS, de Oliveira LCP, da Silva NRC, Fernandes BJT, Dos Santos WP. Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences. Sci Rep 2021; 11:11545. [PMID: 34078924 PMCID: PMC8173023 DOI: 10.1038/s41598-021-90766-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 05/17/2021] [Indexed: 02/04/2023] Open
Abstract
The Covid-19 pandemic, a disease transmitted by the SARS-CoV-2 virus, has already caused the infection of more than 120 million people, of which 70 million have been recovered, while 3 million people have died. The high speed of infection has led to the rapid depletion of public health resources in most countries. RT-PCR is Covid-19's reference diagnostic method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach and represented by co-occurrence matrices. This technique eliminates multiple sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-CoV-2. When comparing SARS-CoV-2 with virus families with similar symptoms, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP classifier and 30% overlap. When SARS-CoV-2 is compared to other coronaviruses and healthy human DNA sequences, we obtained [Formula: see text] for sensitivity and [Formula: see text] for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify DNA sequences for SARS-CoV-2 with greater specificity and sensitivity.
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Affiliation(s)
| | - Aras Ismael Masood
- Information Technology Department, Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq
| | - Leandro Honorato de S Silva
- Escola Politécnica da Universidade de Pernambuco, POLI-UPE, Recife, Brazil
- Instituto Federal de Educação, Ciência e Tecnologia da Paraíba, Campus Cajazeiras, IFPB, Cajazeiras, Brazil
| | | | | | | | | | | | | | - Wellington Pinheiro Dos Santos
- Escola Politécnica da Universidade de Pernambuco, POLI-UPE, Recife, Brazil.
- Departamento de Engenharia Biomédica, Universidade Federal de Pernambuco, DEBM-UFPE, Recife, Brazil.
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73
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Atchadé MN, Sokadjo YM, Moussa AD, Kurisheva SV, Bochenina MV. Cross-Validation Comparison of COVID-19 Forecast Models. ACTA ACUST UNITED AC 2021; 2:296. [PMID: 34056624 PMCID: PMC8150153 DOI: 10.1007/s42979-021-00699-1] [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: 01/05/2021] [Accepted: 05/11/2021] [Indexed: 12/12/2022]
Abstract
Many papers have proposed forecasting models and some are accurate and others are not. Due to the debatable quality of collected data about COVID-19, this study aims to compare univariate time series models with cross-validation and different forecast periods to propose the best one. We used the data titled “Coronavirus Pandemic (COVID-19)” from “‘Our World in Data” about cases for the period of 31 December 2019 to 21 November 2020. The Mean Absolute Percentage Error (MAPE) is computed per model to make the choice of the best fit. Among the univariate models, Error Trend Season (ETS), Exponential smoothing with multiplicative error-trend, and ARIMA; we got that the best one is ETS with additive error-trend and no season. The findings revealed that with the ETS model, we need at least 100 days to have good forecasts with a MAPE threshold of 5%.
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Affiliation(s)
- Mintodê Nicodème Atchadé
- National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Republic of Benin
| | - Yves Morel Sokadjo
- University of Abomey-Calavi/International Chair in Mathematical Physics and Applications (ICMPA: UNESCO-Chair), Abomey-Calavi , Republic of Benin
| | - Aliou Djibril Moussa
- National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Republic of Benin
| | | | - Marina Vladimirovna Bochenina
- Department of Statistics and Econometrics, Saint-Petersburg State University of Economics, Saint-Petersburg , Russia
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74
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Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021; 19:2833-2850. [PMID: 34025952 PMCID: PMC8123783 DOI: 10.1016/j.csbj.2021.05.010] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/01/2021] [Accepted: 05/02/2021] [Indexed: 12/23/2022] Open
Abstract
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
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Key Words
- ABG, Arterial Blood Gas
- ADA, Adenosine Deaminase
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APTT, Activated Partial Thromboplastin Time
- ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer
- AUC, Area Under the Curve
- Acc, Accuracy
- Adaboost, Adaptive Boosting
- Apol AI, Apolipoprotein AI
- Apol B, Apolipoprotein B
- Artificial intelligence
- BNB, Bernoulli Naïve Bayes
- BUN, Blood Urea Nitrogen
- CI, Confidence Interval
- CK-MB, Creatine Kinase isoenzyme
- CNN, Convolutional Neural Networks
- COVID-19
- CPP, COVID-19 Positive Patients
- CRP, C-Reactive Protein
- CRT, Classification and Regression Decision Tree
- CoxPH, Cox Proportional Hazards
- DCNN, Deep Convolutional Neural Networks
- DL, Deep Learning
- DLC, Density Lipoprotein Cholesterol
- DNN, Deep Neural Networks
- DT, Decision Tree
- Diagnosis
- ED, Emergency Department
- ESR, Erythrocyte Sedimentation Rate
- ET, Extra Trees
- FCV, Fold Cross Validation
- FL, Federated Learning
- FiO2, Fraction of Inspiration O2
- GBDT, Gradient Boost Decision Tree
- GBM light, Gradient Boosting Machine light
- GDCNN, Genetic Deep Learning Convolutional Neural Network
- GFR, Glomerular Filtration Rate
- GFS, Gradient boosted feature selection
- GGT, Glutamyl Transpeptidase
- GNB, Gaussian Naïve Bayes
- HDLC, High Density Lipoprotein Cholesterol
- INR, International Normalized Ratio
- Inception Resnet, Inception Residual Neural Network
- L1LR, L1 Regularized Logistic Regression
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LDH, Lactate Dehydrogenase
- LDLC, Low Density Lipoprotein Cholesterol
- LR, Logistic Regression
- LSTM, Long-Short Term Memory
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean corpuscular volume
- ML, Machine Learning
- MLP, MultiLayer Perceptron
- MPV, Mean Platelet Volume
- MRMR, Maximum Relevance Minimum Redundancy
- Multimodal data
- NB, Naïve Bayes
- NLP, Natural Language Processing
- NPV, Negative Predictive Values
- Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer
- OB, Occult Blood test
- PCT, Thrombocytocrit
- PPV, Positive Predictive Values
- PWD, Platelet Distribution Width
- PaO2, Arterial Oxygen Tension
- Paco2, Arterial Carbondioxide Tension
- Prognosis
- RBC, Red Blood Cell
- RBF, Radial Basis Function
- RBP, Retinol Binding Protein
- RDW, Red blood cell Distribution Width
- RF, Random Forest
- RFE, Recursive Feature Elimination
- RSV, Respiratory Syncytial Virus
- SEN, Sensitivity
- SG, Specific Gravity
- SMOTE, Synthetic Minority Oversampling Technique
- SPE, Specificity
- SRLSR, Sparse Rescaled Linear Square Regression
- SVM, Support Vector Machine
- SaO2, Arterial Oxygen saturation
- Screening
- TBA, Total Bile Acid
- TTS, Training Test Split
- WBC, White Blood Cell count
- XGB, eXtreme Gradient Boost
- k-NN, K-Nearest Neighbor
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Affiliation(s)
- Eleni S. Adamidi
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantinos Mitsis
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantina S. Nikita
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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SeyedAlinaghi S, Abbasian L, Solduzian M, Ayoobi Yazdi N, Jafari F, Adibimehr A, Farahani A, Salami Khaneshan A, Ebrahimi Alavijeh P, Jahani Z, Karimian E, Ahmadinejad Z, Khalili H, Seifi A, Ghiasvand F, Ghaderkhani S, Rasoolinejad M. Predictors of the prolonged recovery period in COVID-19 patients: a cross-sectional study. Eur J Med Res 2021; 26:41. [PMID: 33957992 PMCID: PMC8100933 DOI: 10.1186/s40001-021-00513-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 04/22/2021] [Indexed: 02/08/2023] Open
Abstract
Background The clinical course of COVID-19 may vary significantly. The presence of comorbidities prolongs the recovery time. The recovery in patients with mild-to-moderate symptoms might take 10 days, while in those with a critical illness or immunocompromised status could take 15 days. Considering the lack of data about predictors that could affect the recovery time, we conducted this study to identify them. Methods This cross-sectional study was implemented in the COVID-19 clinic of a teaching and referral university hospital in Tehran. Patients with the highly suggestive symptoms who had computed tomography (CT) imaging results with typical findings of COVID-19 or positive results of reverse transcriptase-polymerase chain reaction (RT-PCR) were enrolled in the study. Inpatient and outpatient COVID-19 participants were followed up by regular visits or phone calls, and the recovery period was recorded. Results A total of 478 patients were enrolled. The mean age of patients was 54.11 ± 5.65 years, and 44.2% were female. The median time to recovery was 13.5 days (IQR: 9). Although in the bivariate analysis, multiple factors, including hypertension, fever, diabetes mellitus, gender, and admission location, significantly contributed to prolonging the recovery period, in multivariate analysis, only dyspnea had a significant association with this variable (p = 0.02, the adjusted OR of 2.05; 95% CI 1.12–3.75). Conclusion This study supports that dyspnea is a predictor of recovery time. It seems like optimal management of the comorbidities plays the most crucial role in recovery from COVID-19.
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Affiliation(s)
- SeyedAhmad SeyedAlinaghi
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran
| | - Ladan Abbasian
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran. .,Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran.
| | - Mohammad Solduzian
- Department of Clinical Pharmacy, Faculty of Pharmacy, Tabriz University of Medical Science, Golgasht St, Tabriz, 5166414766, Iran.
| | - Niloofar Ayoobi Yazdi
- Department of Radiology, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Jafari
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Alireza Adibimehr
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Aazam Farahani
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Arezoo Salami Khaneshan
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Parvaneh Ebrahimi Alavijeh
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Zahra Jahani
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Elnaz Karimian
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Zahra Ahmadinejad
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Hossein Khalili
- Department of Clinical Pharmacy, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Arash Seifi
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Fereshteh Ghiasvand
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Sara Ghaderkhani
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
| | - Mehrnaz Rasoolinejad
- Department of Infectious Disease, Imam Khomeini Hospital, Tehran University of Medical Sciences, Blv. Keshavarz, Tehran, 1419733141, Iran
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Haas Q, Alvarez DV, Borissov N, Ferdowsi S, von Meyenn L, Trelle S, Teodoro D, Amini P. Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development. Pharmacology 2021; 106:244-253. [PMID: 33910199 PMCID: PMC8247831 DOI: 10.1159/000515908] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/11/2021] [Indexed: 01/19/2023]
Abstract
INTRODUCTION The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks. METHODS In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time. RESULTS Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention. DISCUSSION The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic.
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Affiliation(s)
- Quentin Haas
- Risklick AG, Spin-off University of Bern, Bern, Switzerland
- Clinical Trial Unit Bern, University of Bern, Bern, Switzerland
| | - David Vicente Alvarez
- HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland
| | - Nikolay Borissov
- Risklick AG, Spin-off University of Bern, Bern, Switzerland
- Clinical Trial Unit Bern, University of Bern, Bern, Switzerland
| | - Sohrab Ferdowsi
- HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland
| | | | - Sven Trelle
- Clinical Trial Unit Bern, University of Bern, Bern, Switzerland
| | - Douglas Teodoro
- HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland
| | - Poorya Amini
- Risklick AG, Spin-off University of Bern, Bern, Switzerland
- Clinical Trial Unit Bern, University of Bern, Bern, Switzerland
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77
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Musulin J, Baressi Šegota S, Štifanić D, Lorencin I, Anđelić N, Šušteršič T, Blagojević A, Filipović N, Ćabov T, Markova-Car E. Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:4287. [PMID: 33919496 PMCID: PMC8073788 DOI: 10.3390/ijerph18084287] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 02/07/2023]
Abstract
COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.
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Affiliation(s)
- Jelena Musulin
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Sandi Baressi Šegota
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Daniel Štifanić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Ivan Lorencin
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Nikola Anđelić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia; (J.M.); (D.Š.); (I.L.); (N.A.)
| | - Tijana Šušteršič
- Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia; (T.Š.); (A.B.); (N.F.)
- Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
| | - Anđela Blagojević
- Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia; (T.Š.); (A.B.); (N.F.)
- Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
| | - Nenad Filipović
- Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia; (T.Š.); (A.B.); (N.F.)
- Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
| | - Tomislav Ćabov
- Faculty of Dental Medicine, University of Rijeka, Krešimirova ul. 40, 51000 Rijeka, Croatia;
| | - Elitza Markova-Car
- Department of Biotechnology, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia;
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78
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Lv Y, Ma C, Li X, Wu M. Big data driven COVID-19 pandemic crisis management: potential approach for global health. Arch Med Sci 2021; 17:829-837. [PMID: 34025856 PMCID: PMC8130465 DOI: 10.5114/aoms/133522] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 02/21/2021] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively. METHODS A systematic review guided by PRISMA guidelines has been performed to obtain the key elements. RESULTS This study identified the thirty-two most relevant documents for qualitative analysis. This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications. It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data. CONCLUSIONS The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis.
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Affiliation(s)
- Yang Lv
- School of Public Administration, Sichuan University, China
| | - Chenwei Ma
- School of Public Administration, Sichuan University, China
| | - Xiaohan Li
- School of Public Administration, Sichuan University, China
| | - Min Wu
- School of Public Administration, Sichuan University, China
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79
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Muhammad M, Ibrahim SA, Yarube IU, Bello B. A REVIEW ON EMERGING PATHOGENESIS OF COVID-19 AND POINTS OF CONCERN FOR RESEARCH COMMUNITIES IN NIGERIA. Afr J Infect Dis 2021; 15:36-43. [PMID: 33889801 PMCID: PMC8052969 DOI: 10.21010/ajid.v15i2.7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND COVID-19 remains an emerging pandemic that continuously poses an alarming threat and challenge to economic, social and well-being of the people throughout the world. It also remains an evolving disease which complete pathogenesis that translates into clinical features is only just emerging by each second of the day. There have been observations about the emerging trends of the disease in Nigeria like in any other country in the world where there is outbreak. This study examined from evidence-based literature the emerging pathogenesis of COVID-19 and important points of concern of the disease in Nigeria. MATERIALS AND METHODS The paper reviewed published articles in PubMed and Google Scholar using search terms 'COVID-19" and "SARS-CoV-2", as well as searched for general COVID-19 information on internet. RESULTS The result summarized literature on emerging pathogenesis of COVID-19 and important points of concern as well as research questions as to the peculiar trends of the disease in Nigeria. CONCLUSION Pathogenesis of COVID-19 remains an emerging knowledge and there are many important research questions that need to be scientifically answered for a successful containment of COVID-19 in Nigeria. It is recommended that all members of intellectual research communities should join the fight against COVID-19 pandemic.
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Affiliation(s)
- Mubarak Muhammad
- Department of Physiology, College of Medicine, University of Ibadan, Nigeria
| | - Salisu Ahmed Ibrahim
- Department of Human Physiology, College of Health Sciences, Bayero University Kano, Nigeria
| | - Isyaku Umar Yarube
- Department of Human Physiology, College of Health Sciences, Bayero University Kano, Nigeria
| | - Bashir Bello
- Department of Physiotherapy, College of Health Sciences, Bayero University Kano, Nigeria
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80
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Godavarthi D, A MS. Classification of covid related articles using machine learning. MATERIALS TODAY. PROCEEDINGS 2021:S2214-7853(21)00571-X. [PMID: 33680869 PMCID: PMC7916526 DOI: 10.1016/j.matpr.2021.01.480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 12/29/2020] [Accepted: 01/17/2021] [Indexed: 10/26/2022]
Abstract
Covid 19 pandemic has placed the entire world in a precarious condition. Earlier it was a serious issue in china whereas now it is being witnessed by citizens all over the world. Scientists are working hard to find treatment and vaccines for the coronavirus, also termed as covid. With the growing literature, it has become a major challenge for the medical community to find answers to questions related to covid-19. We have proposed a machine learning-based system that uses text classification applications of NLP to extract information from the scientific literature. Classification of large textual data makes the searching process easier thus useful for scientists. The main aim of our system is to classify the abstracts related to covid with their respective journals so that a researcher can refer to articles of his interest from the required journals instead of searching all the articles. In this paper, we describe our methodology needed to build such a system. Our system experiments on the COVID-19 open research dataset and the performance is evaluated using classifiers like KNN, MLP, etc. An explainer was also built using XGBoost to show the model predictions.
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Affiliation(s)
- Deepthi Godavarthi
- Dept. of CSSE, Andhra University College of Engineering (A), Visakhapatnam, AP, India
| | - Mary Sowjanya A
- Dept. of CSSE, Andhra University College of Engineering (A), Visakhapatnam, AP, India
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81
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Fuzzy based expert system for diagnosis of coronary artery disease in nigeria. HEALTH AND TECHNOLOGY 2021; 11:319-329. [PMID: 33614390 PMCID: PMC7882232 DOI: 10.1007/s12553-021-00531-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/07/2021] [Indexed: 02/07/2023]
Abstract
Expert system is an artificial intelligence based system that imitates the decision making ability of human and it is used as the diagnostic tool for many diseases including diabetes mellitus, COVID-19, cancers, coronary artery disease (CAD), among other diseases. Even though CAD is globally one of the deadliest diseases and it is not well known in Nigeria, it causes many deaths as such in 2014, 53,836 or 2.82% of total deaths in Nigeria resulted from the CAD. In this study, fuzzy based expert system for diagnosis of CAD is developed in order to provide the complementary diagnostic tools for diagnosis of CAD’s patients in Nigeria. The improved C4.5 data mining algorithm is used to transfer the knowledge of human expert to the knowledge base on the expert system instead of using conventional techniques such as interviews, questionnaires, etc. Taken together, the performance evaluation system was carried out, and the system has an overall accuracy, sensitivity and specificity of 94.55%, 95.35% and 95.00% respectively; which show that, the system is reliable and capable of diagnosing both negative and positive cases of CAD patients efficiently.
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82
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Islam MM, Karray F, Alhajj R, Zeng J. A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19). IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:30551-30572. [PMID: 34976571 PMCID: PMC8675557 DOI: 10.1109/access.2021.3058537] [Citation(s) in RCA: 114] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 02/06/2021] [Indexed: 05/03/2023]
Abstract
Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.
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Affiliation(s)
- Md. Milon Islam
- Centre for Pattern Analysis and Machine IntelligenceDepartment of Electrical and Computer EngineeringUniversity of WaterlooWaterlooONN2L 3G1Canada
| | - Fakhri Karray
- Centre for Pattern Analysis and Machine IntelligenceDepartment of Electrical and Computer EngineeringUniversity of WaterlooWaterlooONN2L 3G1Canada
| | - Reda Alhajj
- Department of Computer ScienceUniversity of CalgaryCalgaryABT2N 1N4Canada
| | - Jia Zeng
- Institute for Personalized Cancer TherapyMD Anderson Cancer CenterHoustonTX77030USA
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Mottaqi MS, Mohammadipanah F, Sajedi H. Contribution of machine learning approaches in response to SARS-CoV-2 infection. INFORMATICS IN MEDICINE UNLOCKED 2021; 23:100526. [PMID: 33869730 PMCID: PMC8044633 DOI: 10.1016/j.imu.2021.100526] [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: 10/16/2020] [Revised: 01/19/2021] [Accepted: 01/20/2021] [Indexed: 12/19/2022] Open
Abstract
Problem The lately emerged SARS-CoV-2 infection, which has put the whole world in an aberrant demanding situation, has generated an urgent need for developing effective responses through artificial intelligence (AI). Aim This paper aims to overview the recent applications of machine learning techniques contributing to prevention, diagnosis, monitoring, and treatment of coronavirus disease (SARS-CoV-2). Methods A progressive investigation of the recent publications up to November 2020, related to AI approaches towards managing the challenges of COVID-19 infection was made. Results For patient diagnosis and screening, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are broadly applied for classification purposes. Moreover, Deep Neural Network (DNN) and homology modeling are the most used SARS-CoV-2 drug repurposing models. Conclusion While the fields of diagnosis of the SARS-CoV-2 infection by medical image processing and its dissemination pattern through machine learning have been sufficiently studied, some areas such as treatment outcome in patients and drug development need to be further investigated using AI approaches.
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Affiliation(s)
- Mohammad Sadeq Mottaqi
- Department of Microbial Biotechnology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, 14155-6455, Tehran, Iran
| | - Fatemeh Mohammadipanah
- Department of Microbial Biotechnology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, 14155-6455, Tehran, Iran
| | - Hedieh Sajedi
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, 14155-6455, Tehran, Iran
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Abstract
Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires the use of statistical methods for analyzing random processes. Part of the information is often hidden from observation or not monitored. That is why many difficulties have arisen in the process of analyzing the collected information. The paper aims to find frequent patterns and parameters affected by COVID-19. The novelty of the paper is hierarchical architecture comprises supervised and unsupervised methods. It allows the development of an ensemble of the methods based on k-means clustering and classification. The best classifiers from the ensemble are random forest with 500 trees and XGBoost. Classification for separated clusters gives us higher accuracy on 4% in comparison with dataset analysis. The proposed approach can be used also for personalized medicine decision support in other domains. The features selection allows us to analyze the following features with the highest impact on COVID-19: age, sex, blood group, had influenza.
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85
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Ullah SMA, Islam MM, Mahmud S, Nooruddin S, Raju SMTU, Haque MR. Scalable Telehealth Services to Combat Novel Coronavirus (COVID-19) Pandemic. ACTA ACUST UNITED AC 2021; 2:18. [PMID: 33426530 PMCID: PMC7786340 DOI: 10.1007/s42979-020-00401-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 11/18/2020] [Indexed: 01/14/2023]
Abstract
An ongoing pandemic, the novel coronavirus disease 2019 (COVID-19) is threatening the nations of the world regardless of health infrastructure conditions. In the age of digital electronic information and telecommunication technology, scalable telehealth services are gaining immense importance by helping to maintain social distances while providing necessary healthcare services. This paper aims to review the various types of scalable telehealth services used to support patients infected by COVID-19 and other diseases during this pandemic. Recently published research papers collected from various sources such as Google Scholar, ResearchGate, PubMed, Scopus, and IEEE Xplore databases using the terms "Telehealth", "Coronavirus", "Scalable" and "COVID-19" are reviewed. The input data and relevant reports for the analysis and assessment of the various aspects of telehealth technology in the COVID-19 pandemic are taken from official websites. We described the available telehealth systems based on their communication media such as mobile networks, social media, and software based models throughout the review. A comparative analysis among the reviewed systems along with necessary challenges and possible future directions are also drawn for the proper selection of affordable technologies. The usage of scalable telehealth systems improves the quality of the healthcare system and also reduces the infection rate while keeping both patients and doctors safe during the pandemic.
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Affiliation(s)
- Shah Muhammad Azmat Ullah
- Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Md Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Saifuddin Mahmud
- Advanced Telerobotics Research Lab, Department of Computer Science, Kent State University, Kent, Ohio USA
| | - Sheikh Nooruddin
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - S M Taslim Uddin Raju
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Md Rezwanul Haque
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
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86
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Comparative analysis of various supervised machine learning techniques for diagnosis of COVID-19. ELECTRONIC DEVICES, CIRCUITS, AND SYSTEMS FOR BIOMEDICAL APPLICATIONS 2021. [PMCID: PMC8084755 DOI: 10.1016/b978-0-323-85172-5.00020-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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87
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Fedorowski JJ. Could amantadine interfere with COVID-19 vaccines based on the LNP-mRNA platform? Arch Med Sci 2021; 17:827-828. [PMID: 34025855 PMCID: PMC8130463 DOI: 10.5114/aoms/134716] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 03/21/2021] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Amantadine is a well-known medication with indications in neurology and infectious diseases. It is currently FDA approved for Parkinson's disease, drug-induced extrapyramidal symptoms, and influenza. METHODS The article is the author's original research hypothesis. RESULTS Because more people are going to be vaccinated and additional similar vaccines are going to be introduced, we should take into consideration the potential of amantadine to interfere with LNP-mRNA COVID-19 vaccine delivery into the target cells. CONCLUSIONS A more cautious approach to the patients taking amantadine as far as vaccination utilizing LNP-mRNA platform should be considered.
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Affiliation(s)
- Jaroslaw J. Fedorowski
- Polish Hospital Federation, Poland
- Collegium Humanum Warsaw Management University, Warsaw, Poland
- College of Medicine and Health Network, University of Vermont, Vermont, United States
- Warsaw Maria Curie-Sklodowska Medical University, Warsaw, Poland
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88
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Muhammad LJ, Algehyne EA, Usman SS, Ahmad A, Chakraborty C, Mohammed IA. Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset. ACTA ACUST UNITED AC 2020; 2:11. [PMID: 33263111 PMCID: PMC7694891 DOI: 10.1007/s42979-020-00394-7] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 11/05/2020] [Indexed: 12/15/2022]
Abstract
COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.
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Affiliation(s)
- L J Muhammad
- Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria
| | - Ebrahem A Algehyne
- Department of Mathematics, University of Tabuk, Tabuk, 71491 Saudi Arabia
| | - Sani Sharif Usman
- Department of Biological Sciences, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria
| | - Abdulkadir Ahmad
- Department of Computer Science, Kano University of Science and Technology, Wudil, Kano Nigeria
| | - Chinmay Chakraborty
- Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, Jharkhand India
| | - I A Mohammed
- Computer Science Department, Yobe StateUniversity, Damaturu, Yobe State Nigeria
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89
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Asraf A, Islam MZ, Haque MR, Islam MM. Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic. SN COMPUTER SCIENCE 2020; 1:363. [PMID: 33163975 PMCID: PMC7607889 DOI: 10.1007/s42979-020-00383-w] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 10/22/2020] [Indexed: 11/30/2022]
Abstract
During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.
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Affiliation(s)
- Amanullah Asraf
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Md. Zabirul Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Md. Rezwanul Haque
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Md. Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
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90
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Islam MM, Mahmud S, Muhammad LJ, Islam MR, Nooruddin S, Ayon SI. Wearable Technology to Assist the Patients Infected with Novel Coronavirus (COVID-19). ACTA ACUST UNITED AC 2020; 1:320. [PMID: 33063058 PMCID: PMC7528718 DOI: 10.1007/s42979-020-00335-4] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 09/17/2020] [Indexed: 02/06/2023]
Abstract
Wearable technology plays a significant role in our daily life as well as in the healthcare industry. The recent coronavirus pandemic has taken the world’s healthcare systems by surprise. Although trials of possible vaccines are underway, it would take a long time before the vaccines are permitted for public use. Most of the government efforts are currently geared towards preventing the spread of the coronavirus and predicting probable hot zones. The essential and healthcare workers are the most vulnerable towards coronavirus infections due to their required proximity to potential coronavirus patients. Wearable technology can potentially assist in these regards by providing real-time remote monitoring, symptoms prediction, contact tracing, etc. The goal of this paper is to discuss the different existing wearable monitoring devices (respiration rate, heart rate, temperature, and oxygen saturation) and respiratory support systems (ventilators, CPAP devices, and oxygen therapy) which are frequently used to assist the coronavirus affected people. The devices are described based on the services they provide, their working procedures as well as comparative analysis of their merits and demerits with cost. A comparative discussion with probable future trends is also drawn to select the best technology for COVID-19 infected patients. It is envisaged that wearable technology is only capable of providing initial treatment that can reduce the spread of this pandemic.
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Affiliation(s)
- Md Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Saifuddin Mahmud
- Department of Computer Science, Kent State University, Kent, Ohio USA
| | - L J Muhammad
- Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria
| | - Md Rabiul Islam
- Department of Electrical and Electronic Engineering, Bangladesh Army University of Engineering and Technology, Natore, 6431 Bangladesh
| | - Sheikh Nooruddin
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Safial Islam Ayon
- Department of Computer Science and Engineering, Green University of Bangladesh, Dhaka, 1207 Bangladesh
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91
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Islam MM, Haque MR, Iqbal H, Hasan MM, Hasan M, Kabir MN. Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/s42979-020-00305-w] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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92
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Islam MM, Ullah SMA, Mahmud S, Raju SMTU. Breathing Aid Devices to Support Novel Coronavirus (COVID-19)Infected Patients. SN COMPUTER SCIENCE 2020; 1:274. [PMID: 33063053 PMCID: PMC7437108 DOI: 10.1007/s42979-020-00300-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 08/10/2020] [Indexed: 12/18/2022]
Abstract
Novel coronavirus (COVID-19), an ongoing pandemic, is threatening the whole population all over the world including the nations having high or low resource health infrastructure. The number of infection as well as death cases are increasing day by day, and outperforming all the records of previously found infectious diseases. This pandemic is imposing specific pressures on the medical system almost the whole globe. The respiration problem is the main complication that a COVID-19 infected patient faced generally. It is a matter of hope that the recent deployment of small-scale technologies like 3D printer, microcontroller, ventilator, Continuous Positive Airway Pressure (CPAP) are mostly used to resolve the problem associated with medical equipment's for breathing. This paper aims to overview the existing technologies which are frequently used to support the infected patients for respiration. We described the most recent developed breathing aid devices such as oxygen therapy devices, ventilator, and CPAP throughout the review. A comparative analysis among the developed devices with necessary challenges and possible future directions are also outlined for the proper selection of affordable technologies. It is expected that this paper would be of great help to the experts who would like to contribute in this area.
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Affiliation(s)
- Md. Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Shah Muhammad Azmat Ullah
- Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
| | - Saifuddin Mahmud
- Department of Computer Science, Kent State University, Kent, Ohio USA
| | - S. M. Taslim Uddin Raju
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203 Bangladesh
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93
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Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. INFORMATICS IN MEDICINE UNLOCKED 2020; 20:100412. [PMID: 32835084 DOI: 10.1101/2020.06.18.20134718] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/07/2020] [Accepted: 08/07/2020] [Indexed: 05/27/2023] Open
Abstract
Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.
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Affiliation(s)
- Md Zabirul Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Md Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Amanullah Asraf
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
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94
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Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. INFORMATICS IN MEDICINE UNLOCKED 2020; 20:100412. [PMID: 32835084 PMCID: PMC7428728 DOI: 10.1016/j.imu.2020.100412] [Citation(s) in RCA: 207] [Impact Index Per Article: 51.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/07/2020] [Accepted: 08/07/2020] [Indexed: 12/15/2022] Open
Abstract
Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.
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Affiliation(s)
- Md Zabirul Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Md Milon Islam
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
| | - Amanullah Asraf
- Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh
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95
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Muhammad LJ, Algehyne EA, Usman SS. Predictive Supervised Machine Learning Models for Diabetes Mellitus. ACTA ACUST UNITED AC 2020; 1:240. [PMID: 33063051 PMCID: PMC7372976 DOI: 10.1007/s42979-020-00250-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 07/10/2020] [Indexed: 02/08/2023]
Abstract
Diabetes mellitus (DM) is one of the deadliest diseases in the world, especially in developed nations. In recent years, it has become rampant in the developing nations such as Nigeria, posing more threats to individuals in the latter than those in the former. More than 415 million people were reported to suffer from DM worldwide as of 2015, with type 2 of the disease accounting for approximately 90% of the cases. The number of people with DM is expected to rise to 592 million by the year 2035. Therefore, DM is one of the growing public health concerns in Nigeria. In this study, the diagnostic dataset of DM type 2 was collected from the Murtala Mohammed Specialist Hospital, Kano, and used to develop predictive supervised machine learning models based on logistic regression, support vector machine, K-nearest neighbor, random forest, naive Bayes and gradient booting algorithms. The random forest predictive learning-based model appeared to be one of the best developed models with 88.76% in terms of accuracy; however, in terms of receiver operating characteristic curve, random forest and gradient booting predictive learning-based models were found to be the best predictive learning models with 86.28% predictive ability, respectively.
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Affiliation(s)
- L J Muhammad
- Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria
| | - Ebrahem A Algehyne
- Department of Mathematics, University of Tabuk, Tabuk, 71491 Saudi Arabia
| | - Sani Sharif Usman
- Department of Biological Sciences, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria
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96
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1651] [Impact Index Per Article: 412.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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