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Alathari MJA, Mashhadany YA, Bakar AAA, Mokhtar MHH, Bin Zan MSD, Arsad N. COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset. J Virol Methods 2024; 330:115011. [PMID: 39154936 DOI: 10.1016/j.jviromet.2024.115011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/14/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combines convolutional neural networks (CNN) with a bi-directional long short-term memory (Bi-LSTM) network, in conjunction with fiber optic data for SARS-CoV-2 Immunoglobulin G (IgG) antibodies. Our research introduces a comprehensive data preprocessing pipeline and evaluates the performance of four different deep learning (DL) algorithms: CNN, CNN-RNN, BiLSTM, and CNN-BiLSTM, in classifying samples as positive or negative for the COVID-19 virus. Among these, the CNN-BiLSTM classifier demonstrated superior performance on the training datasets, achieving an accuracy of 89 %, a recall of 88 %, a precision of 90 %, an F1-score of 89 %, a specificity of 90 %, a geometric mean (G-mean) of 89 %, and a receiver operating characteristic (ROC) of 96 %. In addition, the achieved classification results were compared with those reported in the literature. The findings indicate that the proposed model has promising potential for classifying COVID-19 and could serve as a valuable tool for healthcare professionals. The use of IgG antibodies to detect the virus enhances the specificity and accuracy of the diagnostic tool.
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Affiliation(s)
- Mohammed Jawad Ahmed Alathari
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Yousif Al Mashhadany
- Department of Electrical Engineering, College of Engineering, Anbar University, Anbar 00964, Iraq.
| | - Ahmad Ashrif A Bakar
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Mohd Hadri Hafiz Mokhtar
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Mohd Saiful Dzulkefly Bin Zan
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
| | - Norhana Arsad
- UKM - Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia.
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2
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Mohtasham F, Pourhoseingholi M, Hashemi Nazari SS, Kavousi K, Zali MR. Comparative analysis of feature selection techniques for COVID-19 dataset. Sci Rep 2024; 14:18627. [PMID: 39128991 PMCID: PMC11317481 DOI: 10.1038/s41598-024-69209-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 08/01/2024] [Indexed: 08/13/2024] Open
Abstract
In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in ensuring the accuracy of predictive models by identifying the most influential variables. This study, focusing on a retrospective cohort of 4778 COVID-19 patients from Iran, explores the performance of various FS methods, including filter, embedded, and hybrid approaches, in predicting mortality outcomes. The researchers leveraged 115 routine clinical, laboratory, and demographic features and employed 13 ML models to assess the effectiveness of these FS methods based on classification accuracy, predictive accuracy, and statistical tests. The results indicate that a Hybrid Boruta-VI model combined with the Random Forest algorithm demonstrated superior performance, achieving an accuracy of 0.89, an F1 score of 0.76, and an AUC value of 0.95 on test data. Key variables identified as important predictors of adverse outcomes include age, oxygen saturation levels, albumin levels, neutrophil counts, platelet levels, and markers of kidney function. These findings highlight the potential of advanced FS techniques and ML models in enhancing early disease detection and informing clinical decision-making.
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Affiliation(s)
- Farideh Mohtasham
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - MohamadAmin Pourhoseingholi
- Hearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Center, University of Nottingham, Nottingham, UK
| | - Seyed Saeed Hashemi Nazari
- Department of Epidemiology, School of Public Health & Safety, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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3
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Bhalerao KS, De Silva PIT, Hiniduma K, Grunbaum A, Rozza N, Kremer R, Rusling JF. Microfluidic Immunoarray for Point-of-Care Detection of Cytokines in COVID-19 Patients. ACS OMEGA 2024; 9:29320-29330. [PMID: 39005811 PMCID: PMC11238202 DOI: 10.1021/acsomega.4c00735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 06/03/2024] [Accepted: 06/10/2024] [Indexed: 07/16/2024]
Abstract
The "cytokine storm" often induced in COVID-19 patients contributes to the onset of "acute respiratory distress syndrome" (ARDS) accompanied by lung infection and damage, multiorgan failure, and even death. This large increase in pro-inflammatory cytokines in blood may be related to severity. Rapid, on-demand cytokine analyses can thus be critical to inform treatment plans and improve survival rates. Here, we report a sensitive, low-cost, semiautomated 3D-printed microfluidic immunoarray to detect 2 cytokines and CRP simultaneously in a single 10 μL serum sample in 25 min. Accuracy was validated by analyzing 80 COVID-19 patient serum samples, with results well correlated to a commercial Meso Scale protein immunoassay. Capture antibodies immobilized in detection microwells in a flat well plate-type flow chamber facilitate the immunoassay, with a programmable syringe pump automatically delivering reagents. Chemiluminescence signals were captured in a dark box with a CCD camera integrated for 30 s. This system was optimized to detect inflammation biomarkers IL-6, IFN-γ, and CRP simultaneously in blood serum. Ultralow limits of detection (LODs) of 0.79 fg/mL for IL-6, 4.2 fg/mL for CRP, and 2.7 fg/mL for IFN-γ with dynamic ranges of up to 100 pg/mL were achieved. ROC statistical analyses showed a relatively good diagnostic value related to the samples assigned WHO COVID-19 scores for disease severity, with the best results for IL-6 and CRP. Monitoring these biomarkers for coronavirus severity may allow prediction of disease severity as a basis for critical treatment decisions and better survival rates.
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Affiliation(s)
- Ketki S Bhalerao
- Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States
| | - P I Thilini De Silva
- Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Keshani Hiniduma
- Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Ami Grunbaum
- Department of Medicine, McGill University Health Centre, 1001 Decarie Blvd., Montreal, QC H3A 1A1, Canada
| | - Nicholas Rozza
- Department of Medicine, McGill University Health Centre, 1001 Decarie Blvd., Montreal, QC H3A 1A1, Canada
| | - Richard Kremer
- Department of Medicine, McGill University Health Centre, 1001 Decarie Blvd., Montreal, QC H3A 1A1, Canada
| | - James F Rusling
- Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States
- Institute of Material Science, University of Connecticut, Storrs, Connecticut 06269, United States
- School of Chemistry, National University of Ireland at Galway, Galway H91 TK33, Ireland
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4
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Rashidi HH, Ikram A, Dang LT, Bashir A, Zohra T, Ali A, Tanvir H, Mudassar M, Ravindran R, Akhtar N, Sikandar RI, Umer M, Akhter N, Butt R, Fennell BD, Khan IH. Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings. Sci Rep 2024; 14:14892. [PMID: 38937503 PMCID: PMC11211475 DOI: 10.1038/s41598-024-63707-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.
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Affiliation(s)
- Hooman H Rashidi
- Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh Medical Center, and University of Pittsburgh School of Medicine, Pittsburgh, USA.
| | - Aamer Ikram
- National Institutes of Health, Islamabad, Pakistan
| | - Luke T Dang
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Adnan Bashir
- Health Information Systems Program (HISP), Islamabad, Pakistan
| | | | - Amna Ali
- National Institutes of Health, Islamabad, Pakistan
| | - Hamza Tanvir
- National Institutes of Health, Islamabad, Pakistan
| | | | - Resmi Ravindran
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA
| | - Nasim Akhtar
- Pakistan Institute of Medical Sciences, Islamabad, Pakistan
| | | | - Mohammed Umer
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Naeem Akhter
- Rawalpindi Medical University-Rawalpindi, Rawalpindi, Pakistan
| | - Rafi Butt
- Isolation Hospital and Infectious Treatment Centre, Islamabad, Pakistan
| | - Brandon D Fennell
- Department of Medicine, University of California, San Francisco, USA
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California, 4400 V Street, DavisSacramento, CA, 95817, USA.
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Lima TE, Ferraz MVF, Brito CAA, Ximenes PB, Mariz CA, Braga C, Wallau GL, Viana IFT, Lins RD. Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning. AN ACAD BRAS CIENC 2024; 96:e20230894. [PMID: 38922277 DOI: 10.1590/0001-376520242023089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/22/2024] [Indexed: 06/27/2024] Open
Abstract
The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients' care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This classification is based on the analysis of clinical parameters and routine blood tests, which are not standardized across the globe. Some laboratory test alterations have been associated to COVID-19 severity, although these data are conflicting partly due to the different methodologies used across different studies. This study aimed to construct and validate a disease severity prediction model using machine learning (ML). Seventy-two patients admitted to a Brazilian hospital and diagnosed with COVID-19 through RT-PCR and/or ELISA, and with varying degrees of disease severity, were included in the study. Their electronic medical records and the results from daily blood tests were used to develop a ML model to predict disease severity. Using the above data set, a combination of five laboratorial biomarkers was identified as accurate predictors of COVID-19 severe disease with a ROC-AUC of 0.80 ± 0.13. Those biomarkers included prothrombin activity, ferritin, serum iron, ATTP and monocytes. The application of the devised ML model may help rationalize clinical decision and care.
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Affiliation(s)
- Tayná E Lima
- Fundação Oswaldo Cruz, Instituto Aggeu Magalhães, Departamento de Virologia, Av. Professor Moraes Rego, s/n, Cidade Universitária, 50740-465 Recife, PE, Brazil
| | - Matheus V F Ferraz
- Fundação Oswaldo Cruz, Instituto Aggeu Magalhães, Departamento de Virologia, Av. Professor Moraes Rego, s/n, Cidade Universitária, 50740-465 Recife, PE, Brazil
- Universidade Federal de Pernambuco, Departamento de Química Fundamental, Av. Professor Moraes Rego, s/n, Cidade Universitária, 50740-560 Recife, PE, Brazil
| | - Carlos A A Brito
- Universidade Federal de Pernambuco, Hospital das Clínicas, Av. Professor Moraes Rego, 1235, Cidade Universitária, 50670-901 Recife, PE, Brazil
| | - Pamella B Ximenes
- Hospital dos Servidores Públicos do Estado de Pernambuco, Av. Conselheiro Rosa e Silva, s/n, Espinheiro, 52020-020 Recife, PE, Brazil
| | - Carolline A Mariz
- Fundação Oswaldo Cruz, Instituto Aggeu Magalhães, Departamento de Parasitologia, Av. Professor Moraes Rego, s/n, Cidade Universitária, 50740-465 Recife, PE, Brazil
| | - Cynthia Braga
- Fundação Oswaldo Cruz, Instituto Aggeu Magalhães, Departamento de Parasitologia, Av. Professor Moraes Rego, s/n, Cidade Universitária, 50740-465 Recife, PE, Brazil
| | - Gabriel L Wallau
- Fundação Oswaldo Cruz, Instituto Aggeu Magalhães, Departamento de Entomologia, Av. Professor Moraes Rego, s/n, Cidade Universitária, 50740-465 Recife, PE, Brazil
| | - Isabelle F T Viana
- Fundação Oswaldo Cruz, Instituto Aggeu Magalhães, Departamento de Virologia, Av. Professor Moraes Rego, s/n, Cidade Universitária, 50740-465 Recife, PE, Brazil
| | - Roberto D Lins
- Fundação Oswaldo Cruz, Instituto Aggeu Magalhães, Departamento de Virologia, Av. Professor Moraes Rego, s/n, Cidade Universitária, 50740-465 Recife, PE, Brazil
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6
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Sheikh BUH, Zafar A. Removing Adversarial Noise in X-ray Images via Total Variation Minimization and Patch-Based Regularization for Robust Deep Learning-based Diagnosis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-023-00919-5. [PMID: 38886292 DOI: 10.1007/s10278-023-00919-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/08/2023] [Accepted: 10/18/2023] [Indexed: 06/20/2024]
Abstract
Deep learning has significantly advanced the field of radiology-based disease diagnosis, offering enhanced accuracy and efficiency in detecting various medical conditions through the analysis of complex medical images such as X-rays. This technology's ability to discern subtle patterns and anomalies has proven invaluable for swift and accurate disease identification. The relevance of deep learning in radiology has been particularly highlighted during the COVID-19 pandemic, where rapid and accurate diagnosis is crucial for effective treatment and containment. However, recent research has uncovered vulnerabilities in deep learning models when exposed to adversarial attacks, leading to incorrect predictions. In response to this critical challenge, we introduce a novel approach that leverages total variation minimization to combat adversarial noise within X-ray images effectively. Our focus narrows to COVID-19 diagnosis as a case study, where we initially construct a classification model through transfer learning designed to accurately classify lung X-ray images encompassing no pneumonia, COVID-19 pneumonia, and non-COVID pneumonia cases. Subsequently, we extensively evaluated the model's susceptibility to targeted and un-targeted adversarial attacks by employing the fast gradient sign gradient (FGSM) method. Our findings reveal a substantial reduction in the model's performance, with the average accuracy plummeting from 95.56 to 19.83% under adversarial conditions. However, the experimental results demonstrate the exceptional efficacy of the proposed denoising approach in enhancing the performance of diagnosis models when applied to adversarial examples. Post-denoising, the model exhibits a remarkable accuracy improvement, surging from 19.83 to 88.23% on adversarial images. These promising outcomes underscore the potential of denoising techniques to fortify the resilience and reliability of AI-based COVID-19 diagnostic systems, laying the foundation for their successful deployment in clinical settings.
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Affiliation(s)
- Burhan Ul Haque Sheikh
- Department of Computer Science, Aligarh Muslim University, Uttar Pradesh, Aligarh, 202002, India.
| | - Aasim Zafar
- Department of Computer Science, Aligarh Muslim University, Uttar Pradesh, Aligarh, 202002, India
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7
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Bartenschlager CC, Gassner UM, Römmele C, Brunner JO, Schlögl-Flierl K, Ziethmann P. The AI ethics of digital COVID-19 diagnosis and their legal, medical, technological, and operational managerial implications. Artif Intell Med 2024; 152:102873. [PMID: 38643592 DOI: 10.1016/j.artmed.2024.102873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 01/27/2024] [Accepted: 04/15/2024] [Indexed: 04/23/2024]
Abstract
The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations. The reasons behind this research-application gap are manifold and may imply an interdisciplinary dimension. We argue that the discipline of AI ethics can provide a framework for interdisciplinary discussion and create a roadmap for the application of digital COVID-19 diagnosis, taking into account all disciplinary stakeholders involved. This article proposes such an ethical framework for the practical use of digital COVID-19 diagnosis, considering legal, medical, operational managerial, and technological aspects of the issue in accordance with our diverse research backgrounds and noting the potential of the approach we set out here to guide future research.
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Affiliation(s)
- Christina C Bartenschlager
- Nuremberg Technical University of Applied Sciences Georg Simon Ohm, Keßlerplatz 12, 90489, Germany; Anaesthesiology and Operative Intensive Care Medicine, Faculty of Medicine, University of Augsburg, University Hospital of Augsburg, Stenglinstr. 2, 86156 Augsburg, Germany; Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany.
| | - Ulrich M Gassner
- Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany; Faculty of Law, University of Augsburg, Universitätsstraße 24, 86159 Augsburg, Germany; Research Centre for E-Health Law, Faculty of Law, University of Augsburg, Universitätsstraße 24, 86159 Augsburg, Germany
| | - Christoph Römmele
- Internal Medicine III, Gastroenterology and Infectious Diseases, Augsburg University Hospital, Stenglinstrasse 2, 86156 Augsburg, Germany
| | - Jens O Brunner
- Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany; Department of Technology, Management, and Economics, Technical University of Denmark, Akademivej 358, 127, 2800 Kongens Lyngby, Denmark; Working Group of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Kerstin Schlögl-Flierl
- Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany; Faculty of Catholic Theology, University of Augsburg, Universitätsstraße 10, 86159 Augsburg, Germany; German Ethics Council, Jägerstraße 22/23, 10117 Berlin, Germany; Center for Responsible AI Technologies, University of Augsburg, 86135 Augsburg, Germany
| | - Paula Ziethmann
- Center for Interdisciplinary Health Research, University of Augsburg, 86135 Augsburg, Germany; Center for Responsible AI Technologies, University of Augsburg, 86135 Augsburg, Germany
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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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Tutsoy O, Koç GG. Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification. BMC Bioinformatics 2024; 25:103. [PMID: 38459463 PMCID: PMC10921629 DOI: 10.1186/s12859-024-05729-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 03/04/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND Blood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet. RESULTS This paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified. CONCLUSIONS The results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.
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Affiliation(s)
- Onder Tutsoy
- Adana Alparslan Turkes Science and Technology University, Adana, Turkey.
| | - Gizem Gul Koç
- Adana Alparslan Turkes Science and Technology University, Adana, Turkey
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10
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Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
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Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
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11
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Wang W, Harrou F, Dairi A, Sun Y. Stacked deep learning approach for efficient SARS-CoV-2 detection in blood samples. Artif Intell Med 2024; 148:102767. [PMID: 38325923 DOI: 10.1016/j.artmed.2024.102767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
Identifying COVID-19 through blood sample analysis is crucial in managing the disease and improving patient outcomes. Despite its advantages, the current test demands certified laboratories, expensive equipment, trained personnel, and 3-4 h for results, with a notable false-negative rate of 15%-20%. This study proposes a stacked deep-learning approach for detecting COVID-19 in blood samples to distinguish uninfected individuals from those infected with the virus. Three stacked deep learning architectures, namely the StackMean, StackMax, and StackRF algorithms, are introduced to improve the detection quality of single deep learning models. To counter the class imbalance phenomenon in the training data, the Synthetic Minority Oversampling Technique (SMOTE) algorithm is also implemented, resulting in increased specificity and sensitivity. The efficacy of the methods is assessed by utilizing blood samples obtained from hospitals in Brazil and Italy. Results revealed that the StackMax method greatly boosted the deep learning and traditional machine learning methods' capability to distinguish COVID-19-positive cases from normal cases, while SMOTE increased the specificity and sensitivity of the stacked models. Hypothesis testing is performed to determine if there is a significant statistical difference in the performance between the compared detection methods. Additionally, the significance of blood sample features in identifying COVID-19 is analyzed using the XGBoost (eXtreme Gradient Boosting) technique for feature importance identification. Overall, this methodology could potentially enhance the timely and precise identification of COVID-19 in blood samples.
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Affiliation(s)
- Wu Wang
- Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing 100872, China.
| | - Fouzi Harrou
- King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.
| | - Abdelkader Dairi
- Computer Science Department, University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, 31000, Bir El Djir, Algeria.
| | - Ying Sun
- King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia
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12
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Haque SBU, Zafar A. Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:308-338. [PMID: 38343214 PMCID: PMC11266337 DOI: 10.1007/s10278-023-00916-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/23/2023] [Accepted: 10/08/2023] [Indexed: 03/02/2024]
Abstract
In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the context of radiology images, has emerged as a transformative force. The significance of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), lies in their capacity to rapidly and accurately diagnose diseases from radiology images. This capability has been particularly vital during the COVID-19 pandemic, where rapid and precise diagnosis played a pivotal role in managing the spread of the virus. DL models, trained on vast datasets of radiology images, have showcased remarkable proficiency in distinguishing between normal and COVID-19-affected cases, offering a ray of hope amidst the crisis. However, as with any technological advancement, vulnerabilities emerge. Deep learning-based diagnostic models, although proficient, are not immune to adversarial attacks. These attacks, characterized by carefully crafted perturbations to input data, can potentially disrupt the models' decision-making processes. In the medical context, such vulnerabilities could have dire consequences, leading to misdiagnoses and compromised patient care. To address this, we propose a two-phase defense framework that combines advanced adversarial learning and adversarial image filtering techniques. We use a modified adversarial learning algorithm to enhance the model's resilience against adversarial examples during the training phase. During the inference phase, we apply JPEG compression to mitigate perturbations that cause misclassification. We evaluate our approach on three models based on ResNet-50, VGG-16, and Inception-V3. These models perform exceptionally in classifying radiology images (X-ray and CT) of lung regions into normal, pneumonia, and COVID-19 pneumonia categories. We then assess the vulnerability of these models to three targeted adversarial attacks: fast gradient sign method (FGSM), projected gradient descent (PGD), and basic iterative method (BIM). The results show a significant drop in model performance after the attacks. However, our defense framework greatly improves the models' resistance to adversarial attacks, maintaining high accuracy on adversarial examples. Importantly, our framework ensures the reliability of the models in diagnosing COVID-19 from clean images.
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Affiliation(s)
- Sheikh Burhan Ul Haque
- Department of Computer Science, Aligarh Muslim University, Uttar Pradesh, Aligarh, 202002, India.
| | - Aasim Zafar
- Department of Computer Science, Aligarh Muslim University, Uttar Pradesh, Aligarh, 202002, India
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13
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Farahat IS, Sharafeldeen A, Ghazal M, Alghamdi NS, Mahmoud A, Connelly J, van Bogaert E, Zia H, Tahtouh T, Aladrousy W, Tolba AE, Elmougy S, El-Baz A. An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis. Sci Rep 2024; 14:851. [PMID: 38191606 PMCID: PMC10774502 DOI: 10.1038/s41598-023-51053-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/29/2023] [Indexed: 01/10/2024] Open
Abstract
The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing the correlation between COVID-19 lesions and the level of respiratory support provided to the patients. Computed tomography (CT) imaging will be used to analyze the three levels of respiratory support received by the patient: Level 0 (minimum support), Level 1 (non-invasive support such as soft oxygen), and Level 2 (invasive support such as mechanical ventilation). The system will begin by segmenting the COVID-19 lesions from the CT images and creating an appearance model for each lesion using a 2D, rotation-invariant, Markov-Gibbs random field (MGRF) model. Three MGRF-based models will be created, one for each level of respiratory support. This suggests that the system will be able to differentiate between different levels of severity in COVID-19 patients. The system will decide for each patient using a neural network-based fusion system, which combines the estimates of the Gibbs energy from the three MGRF-based models. The proposed system were assessed using 307 COVID-19-infected patients, achieving an accuracy of [Formula: see text], a sensitivity of [Formula: see text], and a specificity of [Formula: see text], indicating a high level of prediction accuracy.
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Affiliation(s)
- Ibrahim Shawky Farahat
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | | | - Mohammed Ghazal
- Electrical, Computer and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi, UAE
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, USA
| | - James Connelly
- Department of Radiology, University of Louisville, Louisville, USA
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, USA
| | - Huma Zia
- Electrical, Computer and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi, UAE
| | - Tania Tahtouh
- College of Health Sciences, Abu Dhabi University, Abu Dhabi, UAE
| | - Waleed Aladrousy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Ahmed Elsaid Tolba
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
- The Higher Institute of Engineering and Automotive Technology and Energy, Kafr El Sheikh, Egypt
| | - Samir Elmougy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, USA.
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14
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Vaikunta Pai T, Maithili K, Arun Kumar R, Nagaraju D, Anuradha D, Kumar S, Ravuri A, Sunilkumar Reddy T, Sivaram M, Vidhya RG. DKCNN: Improving deep kernel convolutional neural network-based COVID-19 identification from CT images of the chest. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:913-930. [PMID: 38820059 DOI: 10.3233/xst-230424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
BACKGROUND An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease. OBJECTIVE A novel structure known as the Pointwise-Temporal-pointwise convolution unit is developed incorporated with the varying kernel-based depth wise temporal convolution before and after the pointwise convolution operations. METHODS The outcome is optimized by the Slap Swarm algorithm (SSA). The proposed Deep CNN is composed of depth wise temporal convolution and end-to-end automatic detection of disease. First, the datasets SARS-COV-2 Ct-Scan Dataset and CT scan COVID Prediction dataset are preprocessed using the min-max approach and the features are extracted for further processing. RESULTS The experimental analysis is conducted between the proposed and some state-of-art works and stated that the proposed work effectively classifies the disease than the other approaches. CONCLUSION The proposed structural unit is used to design the deep CNN with the increasing kernel sizes. The classification process is improved by the inclusion of depth wise temporal convolutions along with the kernel variation. The computational complexity is reduced by the introduction of stride convolutions are used in the residual linkage among the adjacent structural units.
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Affiliation(s)
- T Vaikunta Pai
- Department of Information Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University), Bangalore, Karnataka, India
| | - K Maithili
- Department of Computer Science and Engineering (Ai & ML), KG Reddy College of Engineering and Technology, Hyderabad, Telangana, India
| | - Ravula Arun Kumar
- Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India
| | - D Nagaraju
- Department of Computer Science and Engineering, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India
| | - D Anuradha
- Department of Computer Science and Business Systems, Panimalar Engineering College, Chennai, India
| | - Shailendra Kumar
- Department of Electronics and Communication Engineering, Integral University Lucknow, Uttar Pradesh, India
| | | | - T Sunilkumar Reddy
- Department of Computer Science and Engineering, Sri Venkatesa Perumal College of Engineering and Technology, Puttur, Andhra Pradesh, India
| | - M Sivaram
- Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha Nagar, Thandalam, Tamil Nadu, India
| | - R G Vidhya
- Department of ECE, HKBKCE, Bangalore, India
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15
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Tay J, Yen YH, Rivera K, Chou EH, Wang CH, Chou FY, Sun JT, Han ST, Tsai TP, Chen YC, Bhakta T, Tsai CL, Lu TC, Huei-Ming Ma M. Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department. West J Emerg Med 2024; 25:67-78. [PMID: 38205987 PMCID: PMC10777189 DOI: 10.5811/westjem.60243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 01/12/2024] Open
Abstract
Introduction Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population. Methods To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23-May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12-June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort. Results In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747-0.822) performed better than gradient boosting (0.774, 95% CI 0.739-0.811) and extra trees classifier (0.72, 95% CI 0.677-0.762). There was no significant difference between the constructed models. Conclusion Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.
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Affiliation(s)
- Joyce Tay
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
| | - Yi-Hsuan Yen
- Baylor Scott and White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas
| | - Kevin Rivera
- Texas Christian University, School of Medicine, Fort Worth, Texas
| | - Eric H Chou
- Baylor Scott and White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas
- Baylor University Medical Center, Department of Emergency Medicine, Dallas, Texas
| | - Chih-Hung Wang
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
| | - Fan-Ya Chou
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
| | - Jen-Tang Sun
- Far Eastern Memorial Hospital, Department of Emergency Medicine, New Taipei City, Taiwan
| | - Shih-Tsung Han
- Chang Gung Memorial Hospital at Linkou, Department of Emergency Medicine, Taoyuan, Taiwan
| | - Tzu-Ping Tsai
- Taipei Veterans General Hospital, Department of Emergency Medicine, Taipei, Taiwan
| | - Yen-Chia Chen
- Taipei Veterans General Hospital, Department of Emergency Medicine, Taipei, Taiwan
| | - Toral Bhakta
- Baylor Scott and White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas
| | - Chu-Lin Tsai
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
| | - Tsung-Chien Lu
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
| | - Matthew Huei-Ming Ma
- National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University, College of Medicine, Department of Emergency Medicine, Taipei, Taiwan
- National Taiwan University Hospital Yunlin Branch, Department of Emergency Medicine, Yunlin County, Taiwan
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16
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Montaha S, Azam S, Bhuiyan MRI, Chowa SS, Mukta MSH, Jonkman M. Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network. Digit Health 2024; 10:20552076241251660. [PMID: 38817843 PMCID: PMC11138200 DOI: 10.1177/20552076241251660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/12/2024] [Indexed: 06/01/2024] Open
Abstract
Objective Early diagnosis of breast cancer can lead to effective treatment, possibly increase long-term survival rates, and improve quality of life. The objective of this study is to present an automated analysis and classification system for breast cancer using clinical markers such as tumor shape, orientation, margin, and surrounding tissue. The novelty and uniqueness of the study lie in the approach of considering medical features based on the diagnosis of radiologists. Methods Using clinical markers, a graph is generated where each feature is represented by a node, and the connection between them is represented by an edge which is derived through Pearson's correlation method. A graph convolutional network (GCN) model is proposed to classify breast tumors into benign and malignant, using the graph data. Several statistical tests are performed to assess the importance of the proposed features. The performance of the proposed GCN model is improved by experimenting with different layer configurations and hyper-parameter settings. Results Results show that the proposed model has a 98.73% test accuracy. The performance of the model is compared with a graph attention network, a one-dimensional convolutional neural network, and five transfer learning models, ten machine learning models, and three ensemble learning models. The performance of the model was further assessed with three supplementary breast cancer ultrasound image datasets, where the accuracies are 91.03%, 94.37%, and 89.62% for Dataset A, Dataset B, and Dataset C (combining Dataset A and Dataset B) respectively. Overfitting issues are assessed through k-fold cross-validation. Conclusion Several variants are utilized to present a more rigorous and fair evaluation of our work, especially the importance of extracting clinically relevant features. Moreover, a GCN model using graph data can be a promising solution for an automated feature-based breast image classification system.
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Affiliation(s)
- Sidratul Montaha
- Department of Computer Science, University of Calgary, Calgary, Canada
| | - Sami Azam
- Faculty of Science and Technology, Charles Darwin University, Casuarina, Australia
| | | | - Sadia Sultana Chowa
- Faculty of Science and Technology, Charles Darwin University, Casuarina, Australia
| | | | - Mirjam Jonkman
- Faculty of Science and Technology, Charles Darwin University, Casuarina, Australia
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17
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Giuste FO, He L, Lais P, Shi W, Zhu Y, Hornback A, Tsai C, Isgut M, Anderson B, Wang MD. Early and fair COVID-19 outcome risk assessment using robust feature selection. Sci Rep 2023; 13:18981. [PMID: 37923795 PMCID: PMC10624921 DOI: 10.1038/s41598-023-36175-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 05/29/2023] [Indexed: 11/06/2023] Open
Abstract
Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.
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Affiliation(s)
- Felipe O Giuste
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Lawrence He
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Peter Lais
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Wenqi Shi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Yuanda Zhu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Andrew Hornback
- School of Computer Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Chiche Tsai
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Monica Isgut
- School of Biology, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Blake Anderson
- Department of Medicine, Emory University, Atlanta, GA, 30322, USA
| | - May D Wang
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA.
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18
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Fan Y, Liu M, Sun G. An interpretable machine learning framework for diagnosis and prognosis of COVID-19. PLoS One 2023; 18:e0291961. [PMID: 37733828 PMCID: PMC10513274 DOI: 10.1371/journal.pone.0291961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 09/10/2023] [Indexed: 09/23/2023] Open
Abstract
Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.
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Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Meng Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Guicong Sun
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
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19
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Huang S, Arpaci I, Al-Emran M, Kılıçarslan S, Al-Sharafi MA. A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability. MULTIMEDIA TOOLS AND APPLICATIONS 2023. [DOI: 10.1007/s11042-023-16349-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 05/22/2023] [Accepted: 07/17/2023] [Indexed: 09/01/2023]
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20
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Erol Doğan G, Uzbaş B. Diagnosis of COVID-19 from blood parameters using convolutional neural network. Soft comput 2023; 27:1-16. [PMID: 37362276 PMCID: PMC10225057 DOI: 10.1007/s00500-023-08508-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
Asymptomatically presenting COVID-19 complicates the detection of infected individuals. Additionally, the virus changes too many genomic variants, which increases the virus's ability to spread. Because there isn't a specific treatment for COVID-19 in a short time, the essential goal is to reduce the virulence of the disease. Blood parameters, which contain essential clinical information about infectious diseases and are easy to access, have an important place in COVID-19 detection. The convolutional neural network (CNN) architecture, which is popular in image processing, produces highly successful results for COVID-19 detection models. When the literature is examined, it is seen that COVID-19 studies with CNN are generally done using lung images. In this study, one-dimensional (1D) blood parameters data were converted into two-dimensional (2D) image data after preprocessing, and COVID-19 detection was made with CNN. The t-distributed stochastic neighbor embedding method was applied to transfer the feature vectors to the 2D plane. All data were framed with convex hull and minimum bounding rectangle algorithms to obtain image data. The image data obtained by pixel mapping was presented to the developed 3-line CNN architecture. This study proposes an effective and successful model by providing a combination of low-cost and rapidly-accessible blood parameters and CNN architecture making image data processing highly successful for COVID-19 detection. Ultimately, COVID-19 detection was made with a success rate of 94.85%. This study has brought a new perspective to COVID-19 detection studies by obtaining 2D image data from 1D COVID-19 blood parameters and using CNN.
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Affiliation(s)
| | - Betül Uzbaş
- Computer Engineering Department, Konya Technical University, Konya, Turkey
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21
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Panjeta M, Reddy A, Shah R, Shah J. Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-28. [PMID: 37362659 PMCID: PMC10224655 DOI: 10.1007/s11042-023-15247-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 03/10/2023] [Accepted: 03/30/2023] [Indexed: 06/28/2023]
Abstract
Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed. Based on these facts, we looked at how the ML and DL-based models can be used to deal with the COVID-19 pandemic problem and what the pros and cons of each are. This paper gives a full look at the different ways to find COVID-19. We looked at the COVID-19 issues in a systematic way and then rated the methods and techniques for finding it based on their availability, ease of use, accuracy, and cost. We have also shown in pictures how well each of the detection techniques works. We did a comparison of different detection models based on the above factors. This helps researchers understand the different methods and the pros and cons of using them as the basis for their research. In the last part, we talk about the open challenges and research questions that come with putting these techniques together with other detection methods.
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Affiliation(s)
- Manisha Panjeta
- Department of Computer Science and Engineering, Thapar Institute of Engineering Technology, Punjab, 147004 India
| | - Aryan Reddy
- Computer Science Department, NMIMS University, Mumbai, India
| | - Rushabh Shah
- Computer Science Department, NMIMS University, Mumbai, India
| | - Jash Shah
- Computer Science Department, NMIMS University, Mumbai, India
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22
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Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics (Basel) 2023; 13:1749. [PMID: 37238232 PMCID: PMC10217633 DOI: 10.3390/diagnostics13101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/29/2023] [Indexed: 05/28/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
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Affiliation(s)
| | - Murat Koyuncu
- Department of Information Systems Engineering, Atilim University, 06830 Ankara, Turkey;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
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Dabbagh R, Jamal A, Bhuiyan Masud JH, Titi MA, Amer YS, Khayat A, Alhazmi TS, Hneiny L, Baothman FA, Alkubeyyer M, Khan SA, Temsah MH. Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review. Cureus 2023; 15:e38373. [PMID: 37265897 PMCID: PMC10230599 DOI: 10.7759/cureus.38373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/30/2023] [Indexed: 06/03/2023] Open
Abstract
During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
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Affiliation(s)
- Rufaidah Dabbagh
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Amr Jamal
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | | | - Maher A Titi
- Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Yasser S Amer
- Pediatrics, Quality Management Department, King Saud University Medical City, Riyadh, SAU
- Research Chair for Evidence-Based Health Care and Knowledge Translation, Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Afnan Khayat
- Health Information Management Department, Prince Sultan Military College of Health Sciences, Al Dhahran, SAU
| | - Taha S Alhazmi
- Family & Community Medicine Department, College of Medicine, King Saud University, Riyadh, SAU
| | - Layal Hneiny
- Medicine, Wegner Health Sciences Library, University of South Dakota, Vermillion, USA
| | - Fatmah A Baothman
- Department of Information Systems, King Abdulaziz University, Jeddah, SAU
| | | | - Samina A Khan
- School of Computer Sciences, Universiti Sains Malaysia, Penang, MYS
| | - Mohamad-Hani Temsah
- Pediatric Intensive Care Unit, Department of Pediatrics, King Saud University, Riyadh, SAU
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Dobrijević D, Antić J, Rakić G, Katanić J, Andrijević L, Pastor K. Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach. CHILDREN (BASEL, SWITZERLAND) 2023; 10:children10050761. [PMID: 37238309 DOI: 10.3390/children10050761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 04/11/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023]
Abstract
BACKGROUND The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. METHODS A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. RESULTS An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. CONCLUSIONS The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.
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Affiliation(s)
- Dejan Dobrijević
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Children and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Jelena Antić
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Children and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Goran Rakić
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Children and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | - Jasmina Katanić
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Institute for Children and Youth Health Care of Vojvodina, 21000 Novi Sad, Serbia
| | | | - Kristian Pastor
- Faculty of Technology, University of Novi Sad, 21000 Novi Sad, Serbia
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Harrou F, Dairi A, Dorbane A, Kadri F, Sun Y. Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests. Diagnostics (Basel) 2023; 13:diagnostics13081466. [PMID: 37189568 DOI: 10.3390/diagnostics13081466] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data.
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Affiliation(s)
- Fouzi Harrou
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Abdelkader Dairi
- Computer Science Department, University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB), El Mnaouar, BP 1505, Oran 31000, Algeria
| | - Abdelhakim Dorbane
- Smart Structures Laboratory (SSL), Department of Mechanical Engineering, Belhadj Bouchaib University of Ain Temouchent, Ain Temouchent 46000, Algeria
| | - Farid Kadri
- Aeroline DATA & CET, Agence 1031, Sopra Steria Group, 31770 Colomiers, France
| | - Ying Sun
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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26
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Chakraborty A, Mitra S, Bhattacharjee M, De D, Pal AJ. Determining human-coronavirus protein-protein interaction using machine intelligence. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2023; 18:100228. [PMID: 37056696 PMCID: PMC10077817 DOI: 10.1016/j.medntd.2023.100228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 03/29/2023] [Accepted: 04/01/2023] [Indexed: 04/08/2023] Open
Abstract
The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus −19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.
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Affiliation(s)
- Arijit Chakraborty
- Bachelor of Computer Application Department, The Heritage Academy, Kolkata, India
| | - Sajal Mitra
- Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India
| | | | - Debashis De
- Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India
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27
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Huyut MT, Huyut Z. Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees. Heliyon 2023; 9:e14015. [PMID: 36919085 PMCID: PMC9985543 DOI: 10.1016/j.heliyon.2023.e14015] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 01/25/2023] [Accepted: 02/17/2023] [Indexed: 03/07/2023] Open
Abstract
Background and objective A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis. Material and method This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease. Results Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients. Conclusions This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.
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Affiliation(s)
- Mehmet Tahir Huyut
- Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Erzincan, Turkey
- Corresponding author. Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Erzincan, Turkey.
| | - Zübeyir Huyut
- Van Yuzuncu Yıl University, Faculty of Medicine, Department of Biochemistry, Van, Turkey
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28
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Altantawy DA, Kishk SS. Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:118935. [PMID: 36210961 PMCID: PMC9527205 DOI: 10.1016/j.eswa.2022.118935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/21/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
SARS-CoV2 (COVID-19) is the virus that causes the pandemic that has severely impacted human society with a massive death toll worldwide. Hence, there is a persistent need for fast and reliable automatic tools to help health teams in making clinical decisions. Predictive models could potentially ease the strain on healthcare systems by early and reliable screening of COVID-19 patients which helps to combat the spread of the disease. Recent studies have reported some key advantages of employing routine blood tests for initial screening of COVID-19 patients. Thus, in this paper, we propose a novel COVID-19 prediction model based on routine blood tests. In this model, we depend on exploiting the real dependency among the employed feature pool by a sparsification procedure. In this sparse domain, a hybrid feature selection mechanism is proposed. This mechanism fuses the selected features from two perspectives, the first is Pearson correlation and the second is a new Minkowski-based equilibrium optimizer (MEO). Then, the selected features are fed into a new 1D Convolutional Neural Network (1DCNN) for a final diagnosis decision. The proposed prediction model is tested with a new public dataset from San Raphael Hospital, Milan, Italy, i.e., OSR dataset which has two sub-datasets. According to the experimental results, the proposed model outperforms the state-of-the-art techniques with an average testing accuracy of 98.5% while we employ only less than half the size of the feature pool, i.e., we need only less than half the given blood tests in the employed dataset to get a final diagnosis decision.
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Affiliation(s)
- Doaa A Altantawy
- Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, 60 El-Gomhoria Street, Mansoura, Egypt
| | - Sherif S Kishk
- Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, 60 El-Gomhoria Street, Mansoura, Egypt
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29
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Tiwari S, Chanak P, Singh SK. A Review of the Machine Learning Algorithms for Covid-19 Case Analysis. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE 2023; 4:44-59. [PMID: 36908643 PMCID: PMC9983698 DOI: 10.1109/tai.2022.3142241] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/25/2021] [Indexed: 11/09/2022]
Abstract
The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure, such as ML and other in handling COVID-19 relevant outbreak difficulties, has been investigated in this article. The major goal of this article is to 1) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. 2) Better grasp the importance of intelligent approaches like ML for the COVID-19 pandemic. 3) The development of improved ML algorithms and types of ML for COVID-19 prognosis. 4) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. 5) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries.
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Affiliation(s)
- Shrikant Tiwari
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
| | - Prasenjit Chanak
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
| | - Sanjay Kumar Singh
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
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Huyut M. Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models. Ing Rech Biomed 2023; 44:100725. [PMID: 35673548 PMCID: PMC9158375 DOI: 10.1016/j.irbm.2022.05.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 04/24/2022] [Accepted: 05/29/2022] [Indexed: 02/07/2023]
Abstract
Objectives When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission. Material and methods The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set. Results In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%. Conclusion The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.
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Chrimes D. Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19. Interact J Med Res 2023; 12:e42540. [PMID: 36645840 PMCID: PMC9888422 DOI: 10.2196/42540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 12/22/2022] [Accepted: 01/10/2023] [Indexed: 01/12/2023] Open
Abstract
COVID-19 has impacted billions of people and health care systems globally. However, there is currently no publicly available chatbot for patients and care providers to determine the potential severity of a COVID-19 infection or the possible biological system responses and comorbidities that can contribute to the development of severe cases of COVID-19. This preliminary investigation assesses this lack of a COVID-19 case-by-case chatbot into consideration when building a decision tree with binary classification that was stratified by age and body system, viral infection, comorbidities, and any manifestations. After reviewing the relevant literature, a decision tree was constructed using a suite of tools to build a stratified framework for a chatbot application and interaction with users. A total of 212 nodes were established that were stratified from lung to heart conditions along body systems, medical conditions, comorbidities, and relevant manifestations described in the literature. This resulted in a possible 63,360 scenarios, offering a method toward understanding the data needed to validate the decision tree and highlighting the complicated nature of severe cases of COVID-19. The decision tree confirms that stratification of the viral infection with the body system while incorporating comorbidities and manifestations strengthens the framework. Despite limitations of a viable clinical decision tree for COVID-19 cases, this prototype application provides insight into the type of data required for effective decision support.
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Affiliation(s)
- Dillon Chrimes
- School of Health Information Science, Human and Social Development, University of Victoria, Victoria, BC, Canada
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Bodaghi A, Fattahi N, Ramazani A. Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases. Heliyon 2023; 9:e13323. [PMID: 36744065 PMCID: PMC9884646 DOI: 10.1016/j.heliyon.2023.e13323] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 01/21/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.
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Affiliation(s)
- Ali Bodaghi
- Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran
| | - Nadia Fattahi
- Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran,Trita Nanomedicine Research and Technology Development Center (TNRTC), Zanjan Health Technology Park, 45156-13191, Zanjan, Iran
| | - Ali Ramazani
- Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran,Department of Biotechnology, Research Institute of Modern Biological Techniques (RIMBT), University of Zanjan, Zanjan, 45371-38791, Iran,Corresponding author. Department of Chemistry, University of Zanjan, Zanjan, 45371-38791, Iran.;
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33
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Mohammadi F, Dehbozorgi L, Akbari‐Hasanjani HR, Joz Abbasalian Z, Akbari‐Hasanjani R, Sabbaghi‐Nadooshan R, Moradi Tabriz H. Evaluation of effective features in the diagnosis of Covid-19 infection from routine blood tests with multilayer perceptron neural network: A cross-sectional study. Health Sci Rep 2023; 6:e1048. [PMID: 36620509 PMCID: PMC9817491 DOI: 10.1002/hsr2.1048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/04/2022] [Accepted: 12/27/2022] [Indexed: 01/09/2023] Open
Abstract
Background and Aim Coronavirus is an infectious disease that is now known as an epidemic, early and accurate diagnosis helps the patient receive more care. The aim of this study is to investigate Covid-19 using blood tests and multilayer perceptron neural network and affective factors in improving and preventing Covid-19. Methods This cross-sectional study was performed on 200 patients referred to Sina Hospital, Tehran, Iran, who were confirmed cases of Covid-19 by computerized tomography-scan analysis between 2 March 2020 to 5 April 2020. After verification of lung involvement, blood sampling was done to separate the sera for C-reactive protein (CRP), magnesium (Mg), lymphocyte percentage, and vitamin D analysis in healthy and unhealthy people. Blood samples from healthy and sick people were applied to the multilayer perceptron network for 70% of the data for training and 30% for testing. Result By examining the features, it was found that in patients with Covid-19, there was a significant relationship between increased CRP and decreased lymphocyte levels, and increased Mg (p < 0.01). In these patients, the amount of CRP and Mg in women and the number of lymphocytes and vitamin D in men were significantly higher (p < 0.01). Conclusion The important advantage of using a multilayer perceptron neural network is to speed up the diagnosis and treatment.
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Affiliation(s)
- Fatemeh Mohammadi
- Department of Pathology, Sina Clinical‐Research CenterTehran University of Medical SciencesTehranIran
| | - Leila Dehbozorgi
- Department of Electrical Engineering, Central Tehran BranchIslamic Azad UniversityTehranIran
| | | | - Zahra Joz Abbasalian
- Department of Pathology, Sina Clinical‐Research CenterTehran University of Medical SciencesTehranIran
| | - Reza Akbari‐Hasanjani
- Department of Electrical Engineering, Central Tehran BranchIslamic Azad UniversityTehranIran
| | - Reza Sabbaghi‐Nadooshan
- Department of Electrical Engineering, Central Tehran BranchIslamic Azad UniversityTehranIran
| | - Hedieh Moradi Tabriz
- Department of Pathology, Sina Clinical‐Research CenterTehran University of Medical SciencesTehranIran
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Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis. BMC Bioinformatics 2023; 24:7. [PMID: 36609221 PMCID: PMC9817417 DOI: 10.1186/s12859-022-05127-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 12/23/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND With the global spread of COVID-19, the world has seen many patients, including many severe cases. The rapid development of machine learning (ML) has made significant disease diagnosis and prediction achievements. Current studies have confirmed that omics data at the host level can reflect the development process and prognosis of the disease. Since early diagnosis and effective treatment of severe COVID-19 patients remains challenging, this research aims to use omics data in different ML models for COVID-19 diagnosis and prognosis. We used several ML models on omics data of a large number of individuals to first predict whether patients are COVID-19 positive or negative, followed by the severity of the disease. RESULTS On the COVID-19 diagnosis task, we got the best AUC of 0.99 with our multilayer perceptron model and the highest F1-score of 0.95 with our logistic regression (LR) model. For the severity prediction task, we achieved the highest accuracy of 0.76 with an LR model. Beyond classification and predictive modeling, our study founds ML models performed better on integrated multi-omics data, rather than single omics. By comparing top features from different omics dataset, we also found the robustness of our model, with a wider range of applicability in diverse dataset related to COVID-19. Additionally, we have found that omics-based models performed better than image or physiological feature-based models, proving the importance of the omics-based dataset for future model development. CONCLUSIONS This study diagnoses COVID-19 positive cases and predicts accurate severity levels. It lowers the dependence on clinical data and professional judgment, by leveraging the utilization of state-of-the-art models. our model showed wider applicability across different omics dataset, which is highly transferable in other respiratory or similar diseases. Hospital and public health care mechanisms can optimize the distribution of medical resources and improve the robustness of the medical system.
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Jeyananthan P. SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach. SN COMPUTER SCIENCE 2023; 4:218. [PMID: 36844504 PMCID: PMC9936926 DOI: 10.1007/s42979-023-01703-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 01/24/2023] [Indexed: 05/02/2023]
Abstract
SARS-CoV-2 pandemic is the big issue of the whole world right now. The health community is struggling to rescue the public and countries from this spread, which revives time to time with different waves. Even the vaccination seems to be not prevents this spread. Accurate identification of infected people on time is essential these days to control the spread. So far, Polymerase chain reaction (PCR) and rapid antigen tests are widely used in this identification, accepting their own drawbacks. False negative cases are the menaces in this scenario. To avoid these problems, this study uses machine learning techniques to build a classification model with higher accuracy to filter the COVID-19 cases from the non-COVID individuals. Transcriptome data of the SARS-CoV-2 patients along with the control are used in this stratification using three different feature selection algorithms and seven classification models. Differently expressed genes also studied between these two groups of people and used in this classification. Results shows that mutual information (or DEGs) along with naïve Bayes (or SVM) gives the best accuracy (0.98 ± 0.04) among these methods. Supplementary Information The online version contains supplementary material available at 10.1007/s42979-023-01703-6.
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36
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Kumar Y, Koul A, Kaur S, Hu YC. Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic. SN COMPUTER SCIENCE 2023; 4:91. [PMID: 36532634 PMCID: PMC9748400 DOI: 10.1007/s42979-022-01493-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/03/2022] [Indexed: 12/15/2022]
Abstract
In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
| | - Apeksha Koul
- Department of Computer Engineering, Punjabi University, Patiala, India
| | - Sukhpreet Kaur
- Department of Computer Science and Engineering, Chandigarh Engineering College, Landran, Mohali India
| | - Yu-Chen Hu
- Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, ROC
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Styrzynski F, Zhakparov D, Schmid M, Roqueiro D, Lukasik Z, Solek J, Nowicki J, Dobrogowski M, Makowska J, Sokolowska M, Baerenfaller K. Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study. Infect Dis Ther 2023; 12:111-129. [PMID: 36333475 PMCID: PMC9638383 DOI: 10.1007/s40121-022-00707-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
INTRODUCTION In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome. METHODS This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications. RESULTS We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome. CONCLUSIONS Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.
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Affiliation(s)
- Filip Styrzynski
- Department of Rheumatology with Subdepartment of Internal Medicine, Medical University of Lodz, 90-419 Lodz, Poland
| | - Damir Zhakparov
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265 Davos, Switzerland ,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Schmid
- University of Applied Sciences of the Grisons, 7000 Chur, Switzerland
| | - Damian Roqueiro
- Swiss Institute of Bioinformatics, Lausanne, Switzerland ,Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Zuzanna Lukasik
- Department of Rheumatology with Subdepartment of Internal Medicine, Medical University of Lodz, 90-419 Lodz, Poland ,Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265 Davos, Switzerland
| | - Julia Solek
- Department of Pathology, Chair of Oncology, Medical University of Lodz, 90-419 Lodz, Poland ,Department of Biostatistics and Translational Medicine, Medical University of Lodz, 90-419 Lodz, Poland
| | - Jakub Nowicki
- Department of Paediatrics, Newborn Pathology and Bone Metabolic Diseases, Medical University of Lodz, 90-419 Lodz, Poland
| | - Milosz Dobrogowski
- Maria Sklodowska-Curie Specialty Voivodeship Hospital, 95-100 Zgierz, Poland
| | - Joanna Makowska
- Department of Rheumatology with Subdepartment of Internal Medicine, Medical University of Lodz, 90-419 Lodz, Poland
| | - Milena Sokolowska
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265 Davos, Switzerland ,Christine Kühne – Center for Allergy Research and Education (CK-CARE), 7265 Davos, Switzerland
| | - Katja Baerenfaller
- Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Herman-Burchard-Strasse 9, 7265 Davos, Switzerland ,Swiss Institute of Bioinformatics, Lausanne, Switzerland
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38
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Kwekha-Rashid AS, Abduljabbar HN, Alhayani B. Coronavirus disease (COVID-19) cases analysis using machine-learning applications. APPLIED NANOSCIENCE 2023; 13:2013-2025. [PMID: 34036034 PMCID: PMC8138510 DOI: 10.1007/s13204-021-01868-7] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 05/04/2021] [Indexed: 12/23/2022]
Abstract
Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.
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Affiliation(s)
- Ameer Sardar Kwekha-Rashid
- Business Information Technology, College of Administration and Economics, University of Sulaimani, Sulaimaniya, Iraq
| | - Heamn N. Abduljabbar
- College of Education, Physics Department, Salahaddin University, Shaqlawa, Iraq ,Department of radiology and imagingFaculty of Medicine and Health Sciences, Universiti Putra Malaysia UPM, Seri Kembangan, Malaysia
| | - Bilal Alhayani
- Electronics and Communication Department, Yildiz Technical University, Istanbul, Turkey
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Erol G, Uzbaş B, Yücelbaş C, Yücelbaş Ş. Analyzing the effect of data preprocessing techniques using machine learning algorithms on the diagnosis of COVID-19. CONCURRENCY AND COMPUTATION : PRACTICE & EXPERIENCE 2022; 34:e7393. [PMID: 36714180 PMCID: PMC9874401 DOI: 10.1002/cpe.7393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/18/2022] [Accepted: 09/08/2022] [Indexed: 06/18/2023]
Abstract
Real-time polymerase chain reaction (RT-PCR) known as the swab test is a diagnostic test that can diagnose COVID-19 disease through respiratory samples in the laboratory. Due to the rapid spread of the coronavirus around the world, the RT-PCR test has become insufficient to get fast results. For this reason, the need for diagnostic methods to fill this gap has arisen and machine learning studies have started in this area. On the other hand, studying medical data is a challenging area because the data it contains is inconsistent, incomplete, difficult to scale, and very large. Additionally, some poor clinical decisions, irrelevant parameters, and limited medical data adversely affect the accuracy of studies performed. Therefore, considering the availability of datasets containing COVID-19 blood parameters, which are less in number than other medical datasets today, it is aimed to improve these existing datasets. In this direction, to obtain more consistent results in COVID-19 machine learning studies, the effect of data preprocessing techniques on the classification of COVID-19 data was investigated in this study. In this study primarily, encoding categorical feature and feature scaling processes were applied to the dataset with 15 features that contain blood data of 279 patients, including gender and age information. Then, the missingness of the dataset was eliminated by using both K-nearest neighbor algorithm (KNN) and chain equations multiple value assignment (MICE) methods. Data balancing has been done with synthetic minority oversampling technique (SMOTE), which is a data balancing method. The effect of data preprocessing techniques on ensemble learning algorithms bagging, AdaBoost, random forest and on popular classifier algorithms KNN classifier, support vector machine, logistic regression, artificial neural network, and decision tree classifiers have been analyzed. The highest accuracies obtained with the bagging classifier were 83.42% and 83.74% with KNN and MICE imputations by applying SMOTE, respectively. On the other hand, the highest accuracy ratio reached with the same classifier without SMOTE was 83.91% for the KNN imputation. In conclusion, certain data preprocessing techniques are examined comparatively and the effect of these data preprocessing techniques on success is presented and the importance of the right combination of data preprocessing to achieve success has been demonstrated by experimental studies.
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Affiliation(s)
- Gizemnur Erol
- Konya Technical UniversitySoftware Engineering DepartmentKonyaTurkey
| | - Betül Uzbaş
- Konya Technical UniversityComputer Engineering DepartmentKonyaTurkey
| | - Cüneyt Yücelbaş
- Tarsus UniversityElectronics and Automation Department, Mersin‐Tarsus OIZ Vocational School of Technical SciencesMersinTurkey
| | - Şule Yücelbaş
- Tarsus UniversityComputer Engineering DepartmentMersinTurkey
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40
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Bartenschlager CC, Ebel SS, Kling S, Vehreschild J, Zabel LT, Spinner CD, Schuler A, Heller AR, Borgmann S, Hoffmann R, Rieg S, Messmann H, Hower M, Brunner JO, Hanses F, Römmele C. COVIDAL: A machine learning classifier for digital COVID-19 diagnosis in German hospitals. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3567431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approx. 4,000 patients and an extraordinary high ratio of SARS-CoV-2 positive patients. We analyze the influence of data preparation, flexibility in optimization targets as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model.
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Affiliation(s)
- Christina C. Bartenschlager
- Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Stefanie S. Ebel
- Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Sebastian Kling
- Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Janne Vehreschild
- Department II of Internal Medicine, Hematology/Oncology, Goethe University, Frankfurt, Frankfurt am Main, Germany; Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; German Centre for Infection Research (DZIF), partner site Bonn-Cologne, Cologne, Germany
| | - Lutz T. Zabel
- Laboratory Medicine, Alb Fils Kliniken GmbH, Eichertstraße 3, 73035 Göppingen, Germany
| | - Christoph D. Spinner
- Technical University of Munich, School of Medicine, University Hospital rechts der Isar, Department of Internal Medicine II, Ismaninger Str. 22, 81675 Munich, Germany
| | - Andreas Schuler
- Gastroenterology, Alb Fils Kliniken GmbH, Eichertstraße 3, 73035 Göppingen, Germany
| | - Axel R. Heller
- Anaesthesiology and Operative Intensive Care Medicine, Medical Faculty, University of Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany
| | | | - Reinhard Hoffmann
- Laboratory Medicine and Microbiology, Medical Faculty, University of Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany
| | - Siegbert Rieg
- Clinic for Internal Medicine II - Infectiology, University Hospital Freiburg, Germany
| | - Helmut Messmann
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Martin Hower
- Department of Pneumology, Infectious Diseases and Intensive Care, Klinikum Dortmund gGmbH, Hospital of University Witten / Herdecke, 44137 Dortmund, Germany
| | - Jens O. Brunner
- Chair of Health Care Operations/Health Information Management, Faculty of Business and Economics, Faculty of Medicine, University of Augsburg, Universitätsstraße 16, 86159 Augsburg, Germany
| | - Frank Hanses
- Emergency Department, University Hospital Regensburg, Germany; Department for Infection Control and Infectious Diseases, University Hospital Regensburg, Germany
| | - Christoph Römmele
- Clinic for Internal Medicine III - Gastroenterology and Infectious Diseases, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
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Cardozo G, Tirloni SF, Pereira Moro AR, Marques JLB. Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2022; 3:e40473. [PMID: 36644762 PMCID: PMC9828303 DOI: 10.2196/40473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/28/2022] [Accepted: 10/31/2022] [Indexed: 11/05/2022]
Abstract
Background In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques. Objective In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases. Methods The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement. Results Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count. Conclusions Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases.
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Affiliation(s)
- Glauco Cardozo
- Federal Institute of Santa Catarina Florianópolis Brazil
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42
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Materón EM, Gómez FR, Almeida MB, Shimizu FM, Wong A, Teodoro KBR, Silva FSR, Lima MJA, Angelim MKSC, Melendez ME, Porras N, Vieira PM, Correa DS, Carrilho E, Oliveira O, Azevedo RB, Goncalves D. Colorimetric Detection of SARS-CoV-2 Using Plasmonic Biosensors and Smartphones. ACS APPLIED MATERIALS & INTERFACES 2022; 14:54527-54538. [PMID: 36454041 PMCID: PMC9728479 DOI: 10.1021/acsami.2c15407] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/08/2022] [Indexed: 05/27/2023]
Abstract
Low-cost, instrument-free colorimetric tests were developed to detect SARS-CoV-2 using plasmonic biosensors with Au nanoparticles functionalized with polyclonal antibodies (f-AuNPs). Intense color changes were noted with the naked eye owing to plasmon coupling when f-AuNPs form clusters on the virus, with high sensitivity and a detection limit of 0.28 PFU mL-1 (PFU stands for plaque-forming units) in human saliva. Plasmon coupling was corroborated with computer simulations using the finite-difference time-domain (FDTD) method. The strategies based on preparing plasmonic biosensors with f-AuNPs are robust to permit SARS-CoV-2 detection via dynamic light scattering and UV-vis spectroscopy without interference from other viruses, such as influenza and dengue viruses. The diagnosis was made with a smartphone app after processing the images collected from the smartphone camera, measuring the concentration of SARS-CoV-2. Both image processing and machine learning algorithms were found to provide COVID-19 diagnosis with 100% accuracy for saliva samples. In subsidiary experiments, we observed that the biosensor could be used to detect the virus in river waters without pretreatment. With fast responses and requiring small sample amounts (only 20 μL), these colorimetric tests can be deployed in any location within the point-of-care diagnosis paradigm for epidemiological control.
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Affiliation(s)
- Elsa M. Materón
- São Carlos Institute of Physics,
University of São Paulo, P.O Box 369,
13560-970São Carlos, SP, Brazil
- São Carlos Institute of Chemistry,
University of São Paulo, 13566-590São Carlos,
SP, Brazil
| | - Faustino R. Gómez
- São Carlos Institute of Physics,
University of São Paulo, P.O Box 369,
13560-970São Carlos, SP, Brazil
| | - Mariana B. Almeida
- São Carlos Institute of Chemistry,
University of São Paulo, 13566-590São Carlos,
SP, Brazil
- National Institute of Science and
Technology in Bioanalytics - INCTBio, 13083-970Campinas, SP,
Brazil
| | - Flavio M. Shimizu
- Department of Applied Physics, “Gleb
Wataghin” Institute of Physics (IFGW), University of Campinas
(UNICAMP), 13083-859Campinas, SP, Brazil
| | - Ademar Wong
- Department of Chemistry, Federal
University of São Carlos (UFSCar), 13560-970São Carlos,
São Paulo, Brazil
| | - Kelcilene B. R. Teodoro
- Nanotechnology National Laboratory for Agriculture,
Embrapa Instrumentation, 13560-970São Carlos, SP,
Brazil
| | - Filipe S. R. Silva
- São Carlos Institute of Chemistry,
University of São Paulo, 13566-590São Carlos,
SP, Brazil
| | - Manoel J. A. Lima
- São Carlos Institute of Chemistry,
University of São Paulo, 13566-590São Carlos,
SP, Brazil
| | - Monara Kaelle S. C. Angelim
- Department of Genetics Evolution, Microbiology, and
Immunology, Institute of Biology, University of Campinas,
13083-970Campinas, SP, Brazil
| | - Matias E. Melendez
- Molecular Carcinogenesis Program,
National Cancer Institute, 20231-050Rio de Janeiro, RJ,
Brazil
| | - Nelson Porras
- Physics Department, del Valle
University, AA 25360Cali, Colombia
| | - Pedro M. Vieira
- Department of Genetics Evolution, Microbiology, and
Immunology, Institute of Biology, University of Campinas,
13083-970Campinas, SP, Brazil
| | - Daniel S. Correa
- Nanotechnology National Laboratory for Agriculture,
Embrapa Instrumentation, 13560-970São Carlos, SP,
Brazil
| | - Emanuel Carrilho
- São Carlos Institute of Chemistry,
University of São Paulo, 13566-590São Carlos,
SP, Brazil
- National Institute of Science and
Technology in Bioanalytics - INCTBio, 13083-970Campinas, SP,
Brazil
| | - Osvaldo
N. Oliveira
- São Carlos Institute of Physics,
University of São Paulo, P.O Box 369,
13560-970São Carlos, SP, Brazil
| | - Ricardo B. Azevedo
- Laboratory of Nanobiotechnology, Department of Genetics
and Morphology, Institute of Biological Sciences, University of
Brasilia, 70910-900Brasilia, DF, Brazil
| | - Débora Goncalves
- São Carlos Institute of Physics,
University of São Paulo, P.O Box 369,
13560-970São Carlos, SP, Brazil
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43
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Cardoso AR, Alves JF, Frasco MF, Piloto AM, Serrano V, Mateus D, Sebastião AI, Matos AM, Carmo A, Cruz T, Fortunato E, Sales MGF. An ultra-sensitive electrochemical biosensor using the Spike protein for capturing antibodies against SARS-CoV-2 in point-of-care. Mater Today Bio 2022; 16:100354. [PMID: 35847374 PMCID: PMC9270181 DOI: 10.1016/j.mtbio.2022.100354] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 11/30/2022] Open
Abstract
This work presents an innovative ultra-sensitive biosensor having the Spike protein on carbon-based screen-printed electrodes (SPEs), for monitoring in point-of-care antibodies against SARS-CoV-2, a very important tool for epidemiological monitoring of COVID-19 infection and establishing vaccination schemes. In an innovative and simple approach, a highly conductive support is combined with the direct adsorption of Spike protein to enable an extensive antibody capture. The high conductivity was ensured by using carboxylated carbon nanotubes on the carbon electrode, by means of a simple and quick approach, which also increased the surface area. These were then modified with EDC/NHS chemistry to produce an amine layer and undergo Spike protein adsorption, to generate a stable layer capable of capturing the antibodies against SARS-CoV-2 in serum with great sensitivity. Electrochemical impedance spectroscopy was used to evaluate the analytical performance of this biosensor in serum. It displayed a linear response between 1.0 pg/mL and 10 ng/mL, with a detection limit of ∼0.7 pg/mL. The analysis of human positive sera containing antibody in a wide range of concentrations yielded accurate data, correlating well with the reference method. It also offered the unique ability of discriminating antibody concentrations in sera below 2.3 μg/mL, the lowest value detected by the commercial method. In addition, a proof-of-concept study was performed by labelling anti-IgG antibodies with quantum dots to explore a new electrochemical readout based on the signal generated upon binding to the anti-S protein antibodies recognised on the surface of the biosensor. Overall, the alternative serologic assay presented is a promising tool for assessing protective immunity to SARS-CoV-2 and a potential guide for revaccination. An ultra-sensitive biosensor for detection of low levels of antibodies against SARS-CoV-2. Highly conductive substrate with adsorbed protein S and point-of-care capability. Application to human sera samples and good correlation with commercial method. Electrochemical impedance readings with an iron-based redox probe. Alternative electrochemical impedance readings with anti-IgG labelled with quantum dots.
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Affiliation(s)
- Ana R Cardoso
- BioMark@UC/CEB - LABBELS, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal.,BioMark@ISEP/ CEB - LABBELS, School of Engineering, Polytechnic Institute of Porto, Porto, Portugal.,CENIMAT
- i3N, Department of Materials Science, School of Science and Technology, NOVA University of Lisbon and CEMOP/UNINOVA, Campus de Caparica, 2829-516, Caparica, Portugal
| | - João Frederico Alves
- BioMark@UC/CEB - LABBELS, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Manuela F Frasco
- BioMark@UC/CEB - LABBELS, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Ana Margarida Piloto
- BioMark@ISEP/ CEB - LABBELS, School of Engineering, Polytechnic Institute of Porto, Porto, Portugal
| | - Verónica Serrano
- BioMark@UC/CEB - LABBELS, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal
| | - Daniela Mateus
- BioMark@UC/CEB - LABBELS, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal.,Center for Neuroscience and Cell Biology, Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal
| | - Ana Isabel Sebastião
- Center for Neuroscience and Cell Biology, Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal
| | - Ana Miguel Matos
- Chemical Engineering Processes and Forest Products Research Center, Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal
| | - Anália Carmo
- Clinical Pathology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Teresa Cruz
- Center for Neuroscience and Cell Biology, Faculty of Pharmacy, University of Coimbra, Coimbra, Portugal
| | - Elvira Fortunato
- CENIMAT
- i3N, Department of Materials Science, School of Science and Technology, NOVA University of Lisbon and CEMOP/UNINOVA, Campus de Caparica, 2829-516, Caparica, Portugal
| | - M Goreti F Sales
- BioMark@UC/CEB - LABBELS, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal.,BioMark@ISEP/ CEB - LABBELS, School of Engineering, Polytechnic Institute of Porto, Porto, Portugal
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Kistenev YV, Vrazhnov DA, Shnaider EE, Zuhayri H. Predictive models for COVID-19 detection using routine blood tests and machine learning. Heliyon 2022; 8:e11185. [PMID: 36311357 PMCID: PMC9595489 DOI: 10.1016/j.heliyon.2022.e11185] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/25/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022] Open
Abstract
The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.
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Velichko A, Huyut MT, Belyaev M, Izotov Y, Korzun D. Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207886. [PMID: 36298235 PMCID: PMC9610709 DOI: 10.3390/s22207886] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/10/2022] [Accepted: 10/14/2022] [Indexed: 05/16/2023]
Abstract
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
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Affiliation(s)
- Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
- Correspondence: (A.V.); (M.T.H.)
| | - Mehmet Tahir Huyut
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, 24000 Erzincan, Türkiye
- Correspondence: (A.V.); (M.T.H.)
| | - Maksim Belyaev
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Yuriy Izotov
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Dmitry Korzun
- Department of Computer Science, Institute of Mathematics and Information Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
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Wen X, Leng P, Wang J, Yang G, Zu R, Jia X, Zhang K, Mengesha BA, Huang J, Wang D, Luo H. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics 2022; 23:387. [PMID: 36153474 PMCID: PMC9509545 DOI: 10.1186/s12859-022-04926-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/13/2022] [Indexed: 11/29/2022] Open
Abstract
The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.
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A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6354579. [PMID: 35990145 PMCID: PMC9391128 DOI: 10.1155/2022/6354579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/04/2022] [Accepted: 07/13/2022] [Indexed: 11/17/2022]
Abstract
Coronavirus (COVID-19) is a highly severe infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). The polymerase chain reaction (PCR) test is essential to confirm the COVID-19 infection, but it has certain limitations, including paucity of reagents, is computationally time-consuming, and requires expert clinicians. Clinicians suggest that the PCR test is not a reliable automated COVID-19 patient detection system. This study proposed a machine learning-based approach to evaluate the PCR role in COVID-19 detection. We collect real data containing 603 COVID-19 samples from the Pakistan Institute of Medical Sciences (PIMS) Hospital in Islamabad, Pakistan, during the third COVID-19 wave. The experiments are separated into two sets. The first set comprises 24 features, including PCR test results, whereas the second comprises 24 features without PCR test. The findings demonstrate that the decision tree achieves the best detection rate for positive and negative COVID-19 patients in both scenarios. The findings reveal that PCR does not contribute to detecting COVID-19 patients. The findings also aid in the early detection of COVID-19, mainly when PCR test results are insufficient for diagnosing COVID-19 and help developing countries with a paucity of PCR tests and specialist facilities.
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Symptom-Based COVID-19 Prognosis through AI-Based IoT: A Bioinformatics Approach. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3113119. [PMID: 35915793 PMCID: PMC9338856 DOI: 10.1155/2022/3113119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 06/17/2022] [Indexed: 11/29/2022]
Abstract
Objective Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come.
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Bioinformatics, Computational Informatics, and Modeling Approaches to the Design of mRNA COVID-19 Vaccine Candidates. COMPUTATION 2022. [DOI: 10.3390/computation10070117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This article is devoted to applying bioinformatics and immunoinformatics approaches for the development of a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. The study’s relevance is dictated by the fact that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began its global threat at the end of 2019 and since then has had a devastating impact on the whole world. Measures to reduce threats from the pandemic include social restrictions, restrictions on international travel, and vaccine development. In most cases, vaccine development depends on the spike glycoprotein, which serves as a medium for its entry into host cells. Although several variants of SARS-CoV-2 have emerged from mutations crossing continental boundaries, about 6000 delta variants have been reported along the coast of more than 20 countries in Africa, with South Africa accounting for the highest percentage. This also applies to the omicron variant of the SARS-CoV-2 virus in South Africa. The authors suggest that bioinformatics and immunoinformatics approaches be used to develop a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. Various immunoinformatics tools have been used to predict T- and B-lymphocyte epitopes. The epitopes were further subjected to multiple evaluations to select epitopes that could elicit a sustained immunological response. The candidate vaccine consisted of seven epitopes, a highly immunogenic adjuvant, an MHC I-targeting domain (MITD), a signal peptide, and linkers. The molecular weight (MW) was predicted to be 223.1 kDa, well above the acceptable threshold of 110 kDa on an excellent vaccine candidate. In addition, the results showed that the candidate vaccine was antigenic, non-allergenic, non-toxic, thermostable, and hydrophilic. The vaccine candidate has good population coverage, with the highest range in East Africa (80.44%) followed by South Africa (77.23%). West Africa and North Africa have 76.65% and 76.13%, respectively, while Central Africa (75.64%) has minimal coverage. Among seven epitopes, no mutations were observed in 100 randomly selected SARS-CoV-2 spike glycoproteins in the study area. Evaluation of the secondary structure of the vaccine constructs revealed a stabilized structure showing 36.44% alpha-helices, 20.45% drawn filaments, and 33.38% random helices. Molecular docking of the TLR4 vaccine showed that the simulated vaccine has a high binding affinity for TLR-4, reflecting its ability to stimulate the innate and adaptive immune response.
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Ahmed MM, Sayed AM, El Abd D, El Sayed IT, Elkholy YS, Fares AH, Fares S. Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals. J Int Med Res 2022; 50:3000605221109392. [PMID: 35861236 PMCID: PMC9310293 DOI: 10.1177/03000605221109392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVES Early detection of coronavirus disease 2019 (COVID-19) is crucial for patients and public health to ensure pandemic control. We aimed to correlate clinical and laboratory data of patients with COVID-19 and their polymerase chain reaction (PCR) results and to assess the accuracy of a deep learning model in diagnosing COVID-19. METHODS This was a retrospective study using an anonymized dataset of patients with suspected COVID-19. Only patients with a complete dataset were included (n = 440). A deep analytics framework and dual-modal approach for PCR-based classification was used, integrating symptoms and laboratory-based modalities. RESULTS Participants with loss of smell or taste were two times more likely to have positive PCR results (odds ratio [OR] 1.86). Participants with neutropenia, high serum ferritin, or monocytosis were three, four, and five times more likely to have positive PCR results (OR 2.69, 4.18, 5.42, respectively). The rate of accuracy achieved using the deep learning framework was 78%, with sensitivity of 83.9% and specificity of 71.4%. CONCLUSION Loss of smell or taste, neutropenia, monocytosis, and high serum ferritin should be routinely assessed with suspected COVID-19 infection. The use of deep learning for diagnosis is a promising tool that can be implemented in the primary care setting.
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Affiliation(s)
- Marwa M Ahmed
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Amal M Sayed
- Department of Clinical & Chemical Pathology, Kasralainy, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Dina El Abd
- Department of Clinical & Chemical Pathology, Kasralainy, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Inas T El Sayed
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Yasmine S Elkholy
- Department of Medical Microbiology & Immunology, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ahmed H Fares
- Computer Science and Engineering Department, Faculty of Engineering, Benha University, Cairo, Egypt
| | - Samar Fares
- Family Medicine Department, Kasralainy Faculty of Medicine, Cairo University, Cairo, Egypt
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