1
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Veras Florentino PT, Araújo VDO, Zatti H, Luis CV, Cavalcanti CRS, de Oliveira MHC, Leão AHFF, Bertoldo Junior J, Barbosa GGC, Ravera E, Cebukin A, David RB, de Melo DBV, Machado TM, Bellei NCJ, Boaventura V, Barral-Netto M, Smaili SS. Text mining method to unravel long COVID's clinical condition in hospitalized patients. Cell Death Dis 2024; 15:671. [PMID: 39271699 PMCID: PMC11399332 DOI: 10.1038/s41419-024-07043-4] [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: 04/13/2024] [Revised: 08/28/2024] [Accepted: 08/29/2024] [Indexed: 09/15/2024]
Abstract
Long COVID is characterized by persistent that extends symptoms beyond established timeframes. Its varied presentation across different populations and healthcare systems poses significant challenges in understanding its clinical manifestations and implications. In this study, we present a novel application of text mining technique to automatically extract unstructured data from a long COVID survey conducted at a prominent university hospital in São Paulo, Brazil. Our phonetic text clustering (PTC) method enables the exploration of unstructured Electronic Healthcare Records (EHR) data to unify different written forms of similar terms into a single phonemic representation. We used n-gram text analysis to detect compound words and negated terms in Portuguese-BR, focusing on medical conditions and symptoms related to long COVID. By leveraging text mining, we aim to contribute to a deeper understanding of this chronic condition and its implications for healthcare systems globally. The model developed in this study has the potential for scalability and applicability in other healthcare settings, thereby supporting broader research efforts and informing clinical decision-making for long COVID patients.
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Affiliation(s)
- Pilar Tavares Veras Florentino
- Laboratório de Medicina e Saúde Pública de Precisão (MeSP2), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Vinícius de Oliveira Araújo
- Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil
| | - Henrique Zatti
- Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Caio Vinícius Luis
- Departamento de Farmacologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | | | | | | | - Juracy Bertoldo Junior
- Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - George G Caique Barbosa
- Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Ernesto Ravera
- Departamento de Farmacologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Alberto Cebukin
- Departamento de Farmacologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Renata Bernardes David
- Departamento de Farmacologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | | | - Tales Mota Machado
- Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Diretoria de Tecnologia da Informação, Universidade Federal de Ouro Preto, Ouro Preto, Brazil
| | - Nancy C J Bellei
- Disciplina de Moléstias Infecciosas, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Viviane Boaventura
- Laboratório de Medicina e Saúde Pública de Precisão (MeSP2), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil
| | - Manoel Barral-Netto
- Laboratório de Medicina e Saúde Pública de Precisão (MeSP2), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil.
- Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, Brazil.
| | - Soraya S Smaili
- Departamento de Farmacologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil.
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Domènech-Montoliu S, Pac-Sa MR, Sala-Trull D, Del Rio-González A, Sanchéz-Urbano M, Satorres-Martinez P, Blasco-Gari R, Casanova-Suarez J, Gil-Fortuño M, López-Diago L, Notari-Rodríguez C, Pérez-Olaso Ó, Romeu-Garcia MA, Ruiz-Puig R, Aleixandre-Gorriz I, Domènech-León C, Arnedo-Pena A. Underreporting of Cases in the COVID-19 Outbreak of Borriana (Spain) during Mass Gathering Events in March 2020: A Cross-Sectional Study. EPIDEMIOLOGIA 2024; 5:499-510. [PMID: 39189253 PMCID: PMC11348374 DOI: 10.3390/epidemiologia5030034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 08/02/2024] [Accepted: 08/06/2024] [Indexed: 08/28/2024] Open
Abstract
Determining the number of cases of an epidemic is the first function of epidemiological surveillance. An important underreporting of cases was observed in many locations during the first wave of the COVID-19 pandemic. To estimate this underreporting in the COVID-19 outbreak of Borriana (Valencia Community, Spain) in March 2020, a cross-sectional study was performed in June 2020 querying the public health register. Logistic regression models were used. Of a total of 468 symptomatic COVID-19 cases diagnosed in the outbreak through anti-SARS-CoV-2 serology, 36 cases were reported (7.7%), resulting in an underreporting proportion of 92.3% (95% confidence interval [CI], 89.5-94.6%), with 13 unreported cases for every reported case. Only positive SARS-CoV-2 polymerase chain reaction cases were predominantly reported due to a limited testing capacity and following a national protocol. Significant factors associated with underreporting included no medical assistance for COVID-19 disease, with an adjusted odds ratio [aOR] of 10.83 (95% CI 2.49-47.11); no chronic illness, aOR = 2.81 (95% CI 1.28-6.17); middle and lower social classes, aOR = 3.12 (95% CI 1.42-6.85); younger age, aOR = 0.97 (95% CI 0.94-0.99); and a shorter duration of illness, aOR = 0.98 (95% CI 0.97-0.99). To improve the surveillance of future epidemics, new approaches are recommended.
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Affiliation(s)
| | - Maria Rosario Pac-Sa
- Public Health Center, 12003 Castelló de la Plana, Spain; (M.R.P.-S.); (M.A.R.-G.)
| | - Diego Sala-Trull
- Emergency Service University Hospital de la Plana, 12540 Vila-Real, Spain; (D.S.-T.); (M.S.-U.); (P.S.-M.); (R.B.-G.); (C.N.-R.); (R.R.-P.)
| | | | - Manuel Sanchéz-Urbano
- Emergency Service University Hospital de la Plana, 12540 Vila-Real, Spain; (D.S.-T.); (M.S.-U.); (P.S.-M.); (R.B.-G.); (C.N.-R.); (R.R.-P.)
| | - Paloma Satorres-Martinez
- Emergency Service University Hospital de la Plana, 12540 Vila-Real, Spain; (D.S.-T.); (M.S.-U.); (P.S.-M.); (R.B.-G.); (C.N.-R.); (R.R.-P.)
| | - Roser Blasco-Gari
- Emergency Service University Hospital de la Plana, 12540 Vila-Real, Spain; (D.S.-T.); (M.S.-U.); (P.S.-M.); (R.B.-G.); (C.N.-R.); (R.R.-P.)
| | | | - Maria Gil-Fortuño
- Microbiology Service University Hospital de la Plana, 12540 Vila-Real, Spain; (M.G.-F.); (Ó.P.-O.)
| | - Laura López-Diago
- Clinical Analysis Service University Hospital de la Plana, 12540 Vila-Real, Spain; (L.L.-D.); (I.A.-G.)
| | - Cristina Notari-Rodríguez
- Emergency Service University Hospital de la Plana, 12540 Vila-Real, Spain; (D.S.-T.); (M.S.-U.); (P.S.-M.); (R.B.-G.); (C.N.-R.); (R.R.-P.)
| | - Óscar Pérez-Olaso
- Microbiology Service University Hospital de la Plana, 12540 Vila-Real, Spain; (M.G.-F.); (Ó.P.-O.)
| | | | - Raquel Ruiz-Puig
- Emergency Service University Hospital de la Plana, 12540 Vila-Real, Spain; (D.S.-T.); (M.S.-U.); (P.S.-M.); (R.B.-G.); (C.N.-R.); (R.R.-P.)
| | - Isabel Aleixandre-Gorriz
- Clinical Analysis Service University Hospital de la Plana, 12540 Vila-Real, Spain; (L.L.-D.); (I.A.-G.)
| | - Carmen Domènech-León
- Department of Medicine, University CEU Cardenal Herrera, 12006 Castelló de la Plana, Spain;
| | - Alberto Arnedo-Pena
- Public Health Center, 12003 Castelló de la Plana, Spain; (M.R.P.-S.); (M.A.R.-G.)
- Department of Health Science, Public University Navarra, 31006 Pamplona, Spain
- Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
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3
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Garavand A, Ameri F, Salehi F, Talebi AH, Karbasi Z, Sabahi A. A Systematic Review of Health Management Mobile Applications in COVID-19 Pandemic: Features, Advantages, and Disadvantages. BIOMED RESEARCH INTERNATIONAL 2024; 2024:8814869. [PMID: 38230030 PMCID: PMC10791194 DOI: 10.1155/2024/8814869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 12/01/2023] [Accepted: 12/28/2023] [Indexed: 01/18/2024]
Abstract
Introduction With the increasing accessibility of smartphones, their use has been considered in healthcare services. Mobile applications have played a pivotal role in providing health services during COVID-19. This study is aimed at identifying the features, advantages, and disadvantages of health management mobile applications during COVID-19. Methods This systematic review was conducted in PubMed, Scopus, and Web of Science using the related keywords up to November 2021. The original articles in English about the health management mobile applications in COVID-19 were selected. The study selection was done by two researchers independently according to inclusion and exclusion criteria. Data extraction was done using a data extraction form, and the results were summarized and reported in related tables and figures. Results Finally, 12 articles were included based on the criteria. The benefits of mobile health applications for health management during COVID-19 were in four themes and 19 subthemes, and the most advantages of the application were in disease management and the possibility of recording information by users, digital tracking of calls, and data confidentiality. Furthermore, the disadvantages of them have been presented in two themes and 14 subthemes. The most common disadvantages are reduced adherence to daily symptom reports, personal interpretation of questions, and result bias. Conclusion The study results showed that mobile applications have been effective in controlling the prevalence of COVID-19 by identifying virus-infested environments, identifying and monitoring infected people, controlling social distancing, and maintaining quarantine. It is suggested that usability, ethical and security considerations, protection of personal information, and privacy of users be considered in application design and development.
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Affiliation(s)
- Ali Garavand
- Health Information Management, Department of Health Information Technology, School of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran
| | - Fatemeh Ameri
- Health Information Technology, Student Research Committee, Department of Health Information Technology, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Salehi
- Health Information Management, Emam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ali Hajipour Talebi
- Health Information Technology Expert, AJA University of Medical Sciences, Tehran, Iran
| | - Zahra Karbasi
- Health Information Management, School of Management and Medical Informatics, Kerman University of Medical Sciences, Kerman, Iran
| | - Azam Sabahi
- Health Information Management, Department of Health Information Technology, Ferdows School of Health and Allied Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran
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Weerarathna IN, Luharia A, Tivaskar S, Nankong FA, Raymond D. Emerging Applications of Biomedical Science in Pandemic Prevention and Control: A Review. Cureus 2023; 15:e44075. [PMID: 37750154 PMCID: PMC10518042 DOI: 10.7759/cureus.44075] [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: 08/09/2023] [Accepted: 08/24/2023] [Indexed: 09/27/2023] Open
Abstract
The COVID-19 pandemic has made it abundantly clear how crucial biomedical science is to pandemic control and prevention on a global scale. The importance of biomedical science in the fight against pandemics has increased with the appearance of new, deadly infectious diseases. Biomedical science and engineering have been presented as possible areas for combating the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to the unique challenges raised by the pandemic, as reported by epidemiologists, immunologists, and doctors, including the survival, symptoms, protein surface composition, and infection mechanisms of COVID-19. These multidisciplinary engineering concepts are applied to design and develop prevention methods, diagnostics, monitoring, and therapies. An infectious disease outbreak that has spread over a sizable region, such as several continents or the entire world, and is affecting a sizable number of people is referred to as a "pandemic. While current knowledge about the SARS-CoV-2 virus is still limited, various (old and new) biomedical approaches have been developed and tested. Here, we review the emerging applications of biomedical science in pandemic prevention and control, including rapid diagnosis tests, the development of vaccines, antiviral therapies, artificial intelligence, genome sequencing, and personal protective equipment. Biomedical science and nanotechnology are two fields that have the potential to combine to develop emerging applications for combating pandemics. In this review, we also discuss the intersection of biomedical science and nanotechnology in pandemic prevention and control.
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Affiliation(s)
- Induni N Weerarathna
- Biomedical Sciences, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Anurag Luharia
- Medical Physics, Radiology, Radiotherapy, Nuclear Medicine, Radiobiology, and Radiation Safety, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Suhas Tivaskar
- Radiology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Francis A Nankong
- Science and Technology, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - David Raymond
- Computer Science and Medical Engineering, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Kalantar R, Hindocha S, Hunter B, Sharma B, Khan N, Koh DM, Ahmed M, Aboagye EO, Lee RW, Blackledge MD. Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19. Sci Rep 2023; 13:10568. [PMID: 37386097 PMCID: PMC10310777 DOI: 10.1038/s41598-023-36712-1] [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: 09/15/2022] [Accepted: 06/07/2023] [Indexed: 07/01/2023] Open
Abstract
Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss' Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications.
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Affiliation(s)
- Reza Kalantar
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
| | - Sumeet Hindocha
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
- AI for Healthcare Centre for Doctoral Training, Imperial College London, Exhibition Road, London, SW7 2BX, UK
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Benjamin Hunter
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Bhupinder Sharma
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Nasir Khan
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Dow-Mu Koh
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Merina Ahmed
- Lung Unit, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Eric O Aboagye
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Richard W Lee
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Matthew D Blackledge
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK.
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Graeßner M, Jungwirth B, Frank E, Schaller SJ, Kochs E, Ulm K, Blobner M, Ulm B, Podtschaske AH, Kagerbauer SM. Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data. Sci Rep 2023; 13:7128. [PMID: 37130884 PMCID: PMC10153050 DOI: 10.1038/s41598-023-33981-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 04/21/2023] [Indexed: 05/04/2023] Open
Abstract
Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-learning-based model to assess the patient's individual risk of postoperative mortality based on preoperative data to allow analysis of personal risk factors. After ethical approval, a model for prediction of postoperative in-hospital mortality based on preoperative data of 66,846 patients undergoing elective non-cardiac surgery between June 2014 and March 2020 was created with extreme gradient boosting. Model performance and the most relevant parameters were shown using receiver operating characteristic (ROC-) and precision-recall (PR-) curves and importance plots. Individual risks of index patients were presented in waterfall diagrams. The model included 201 features and showed good predictive abilities with an area under receiver operating characteristic (AUROC) curve of 0.95 and an area under precision-recall curve (AUPRC) of 0.109. The feature with the highest information gain was the preoperative order for red packed cell concentrates followed by age and c-reactive protein. Individual risk factors could be identified on patient level. We created a highly accurate and interpretable machine learning model to preoperatively predict the risk of postoperative in-hospital mortality. The algorithm can be used to identify factors susceptible to preoperative optimization measures and to identify risk factors influencing individual patient risk.
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Affiliation(s)
- Martin Graeßner
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany
| | - Bettina Jungwirth
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany
| | - Elke Frank
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany
- Commercial department, Klinikum rechts der isar, Technical University of Munich, Munich, Germany
| | - Stefan Josef Schaller
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Anaesthesiology and Operative Intensive Care Medicine (CVK, CCM), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Eberhard Kochs
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Kurt Ulm
- Department of Medical Statistics and Epidemiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Manfred Blobner
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany
| | - Bernhard Ulm
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany
| | - Armin Horst Podtschaske
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Simone Maria Kagerbauer
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany.
- Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Germany.
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7
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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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8
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Han X, Hu Z, Wang S, Zhang Y. A Survey on Deep Learning in COVID-19 Diagnosis. J Imaging 2022; 9:1. [PMID: 36662099 PMCID: PMC9866755 DOI: 10.3390/jimaging9010001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022] Open
Abstract
According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.
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Affiliation(s)
- Xue Han
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Zuojin Hu
- School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
| | - Shuihua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
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Severe COVID-19 is characterised by inflammation and immature myeloid cells early in disease progression. Heliyon 2022; 8:e09230. [PMID: 35386227 PMCID: PMC8973020 DOI: 10.1016/j.heliyon.2022.e09230] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/20/2021] [Accepted: 03/28/2022] [Indexed: 12/21/2022] Open
Abstract
SARS-CoV-2 infection causes a wide spectrum of disease severity. Identifying the immunological characteristics of severe disease and the risk factors for their development are important in the management of COVID-19. This study aimed to identify and rank clinical and immunological features associated with progression to severe COVID-19 in order to investigate an immunological signature of severe disease. One hundred and eight patients with positive SARS-CoV-2 PCR were recruited. Routine clinical and laboratory markers were measured, as well as myeloid and lymphoid whole-blood immunophenotyping and measurement of the pro-inflammatory cytokines IL-6 and soluble CD25. All analysis was carried out in a routine hospital diagnostic laboratory. Univariate analysis demonstrated that severe disease was most strongly associated with elevated CRP and IL-6, loss of DLA-DR expression on monocytes and CD10 expression on neutrophils. Unbiased machine learning demonstrated that these four features were strongly associated with severe disease, with an average prediction score for severe disease of 0.925. These results demonstrate that these four markers could be used to identify patients developing severe COVID-19 and allow timely delivery of therapeutics. Severe COVID-19 is characterised by a combination of emergency myelopoiesis and inflammation. These changes can be rapidly identified in a diagnostic laboratory, facilitating intervention. This disease signature was derived from a cohort of patients with a wide range of ages, frailty and COVID-19 severity.
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Mi J, Han X, Wang R, Ma R, Zhao D. Diagnostic Accuracy of Wireless Capsule Endoscopy in Polyp Recognition Using Deep Learning: A Meta-Analysis. Int J Clin Pract 2022; 2022:9338139. [PMID: 35685533 PMCID: PMC9159236 DOI: 10.1155/2022/9338139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/18/2022] [Accepted: 02/25/2022] [Indexed: 12/24/2022] Open
Abstract
AIM As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identifying polyps using deep learning. METHOD Two independent reviewers searched PubMed, Embase, the Web of Science, and the Cochrane Library for potentially eligible studies published up to December 8, 2021, which were analysed on a per-image basis. STATA RevMan and Meta-DiSc were used to conduct this meta-analysis. A random effects model was used, and a subgroup and regression analysis was performed to explore sources of heterogeneity. RESULTS Eight studies published between 2017 and 2021 included 819 patients, and 18,414 frames were eventually included in the meta-analysis. The summary estimates for the WCE in identifying polyps by deep learning were sensitivity 0.97 (95% confidence interval (CI), 0.95-0.98); specificity 0.97 (95% CI, 0.94-0.98); positive likelihood ratio 27.19 (95% CI, 15.32-50.42); negative likelihood ratio 0.03 (95% CI 0.02-0.05); diagnostic odds ratio 873.69 (95% CI, 387.34-1970.74); and the area under the sROC curve 0.99. CONCLUSION WCE uses deep learning to identify polyps with high accuracy, but multicentre prospective randomized controlled studies are needed in the future.
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Affiliation(s)
- Junjie Mi
- Digestive Endoscopy Center, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Xiaofang Han
- Reproductive Medicine, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Rong Wang
- Digestive Endoscopy Center, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Ruijun Ma
- Digestive Endoscopy Center, Shanxi Provincial People's Hospital, Taiyuan, China
| | - Danyu Zhao
- Digestive Endoscopy Center, Shanxi Provincial People's Hospital, Taiyuan, China
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