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Chen W, Yang J, Sun Z, Zhang X, Tao G, Ding Y, Gu J, Bu J, Wang H. DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data. Transl Psychiatry 2024; 14:375. [PMID: 39277595 PMCID: PMC11401938 DOI: 10.1038/s41398-024-02972-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 04/23/2024] [Accepted: 05/31/2024] [Indexed: 09/17/2024] Open
Abstract
Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring comorbidities that seriously affect mental health. Precisely diagnosis of ASD is crucial to intervention and rehabilitation. A single modality may not fully reflect the complex mechanisms underlying ASD, and combining multiple modalities enables a more comprehensive understanding. Here, we propose, DeepASD, an end-to-end trainable regularized graph learning method for ASD prediction, which incorporates heterogeneous multimodal data and latent inter-patient relationships to better understand the pathogenesis of ASD. DeepASD first learns cross-modal feature representations through a multimodal adversarial-regularized encoder, and then constructs adaptive patient similarity networks by leveraging the representations of each modality. DeepASD exploits inter-patient relationships to boost the ASD diagnosis that is implemented by a classifier compositing of graph neural networks. We apply DeepASD to the benchmarking Autism Brain Imaging Data Exchange (ABIDE) data with four modalities. Experimental results show that the proposed DeepASD outperforms eight state-of-the-art baselines on the benchmarking ABIDE data, showing an improvement of 13.25% in accuracy, 7.69% in AUC-ROC, and 17.10% in specificity. DeepASD holds promise for a more comprehensive insight of the complex mechanisms of ASD, leading to improved diagnosis performance.
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Affiliation(s)
- Wanyi Chen
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
| | - Jianjun Yang
- Department of General Practice, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong, China
| | - Zhongquan Sun
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiang Zhang
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Guangyu Tao
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuan Ding
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingjun Gu
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
- Pujian Technology, Hangzhou, Zhejiang, China
| | - Jiajun Bu
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
| | - Haishuai Wang
- Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, China.
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Qian FH, Cao Y, Liu YX, Huang J, Zhu RH. A predictive model to explore risk factors for severe COVID-19. Sci Rep 2024; 14:18197. [PMID: 39107340 PMCID: PMC11303808 DOI: 10.1038/s41598-024-68946-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 07/30/2024] [Indexed: 08/10/2024] Open
Abstract
With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the "rms" package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.
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Affiliation(s)
- Fen-Hong Qian
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, No.438, Jiefang Road, Jingkou District, Zhenjiang, Jiangsu, China.
| | - Yu Cao
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, No.438, Jiefang Road, Jingkou District, Zhenjiang, Jiangsu, China
| | - Yu-Xue Liu
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, No.438, Jiefang Road, Jingkou District, Zhenjiang, Jiangsu, China
| | - Jing Huang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, No.438, Jiefang Road, Jingkou District, Zhenjiang, Jiangsu, China
| | - Rong-Hao Zhu
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, No.438, Jiefang Road, Jingkou District, Zhenjiang, Jiangsu, China
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Li C, Wu K, Yang R, Liao M, Li J, Zhu Q, Zhang J, Zhang X. Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19. Sci Rep 2024; 14:8450. [PMID: 38600309 PMCID: PMC11006847 DOI: 10.1038/s41598-024-59117-0] [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: 01/04/2024] [Accepted: 04/08/2024] [Indexed: 04/12/2024] Open
Abstract
The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.
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Affiliation(s)
- Chunyu Li
- Department of Respiratory and Critical Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Ke Wu
- Department of Respiratory and Critical Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Rui Yang
- Department of Internal Medicine, Guiyang First People's Hospital, Guiyang, 550004, Guizhou, China
| | - Minghua Liao
- Department of Respiratory and Critical Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Jun Li
- Department of Respiratory and Critical Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Qian Zhu
- Department of Respiratory and Critical Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Jiayi Zhang
- Department of Respiratory and Critical Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China
- School of Clinical Medicine, Guizhou Medical University, Guiyang, 550004, Guizhou, China
| | - Xianming Zhang
- Department of Respiratory and Critical Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550004, Guizhou, China.
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Liu P, Xing Z, Peng X, Zhang M, Shu C, Wang C, Li R, Tang L, Wei H, Ran X, Qiu S, Gao N, Yeo YH, Liu X, Ji F. Machine learning versus multivariate logistic regression for predicting severe COVID-19 in hospitalized children with Omicron variant infection. J Med Virol 2024; 96:e29447. [PMID: 38305064 DOI: 10.1002/jmv.29447] [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: 05/08/2023] [Revised: 01/02/2024] [Accepted: 01/23/2024] [Indexed: 02/03/2024]
Abstract
With the emergence of the Omicron variant, the number of pediatric Coronavirus Disease 2019 (COVID-19) cases requiring hospitalization and developing severe or critical illness has significantly increased. Machine learning and multivariate logistic regression analysis were used to predict risk factors and develop prognostic models for severe COVID-19 in hospitalized children with the Omicron variant in this study. Of the 544 hospitalized children including 243 and 301 in the mild and severe groups, respectively. Fever (92.3%) was the most common symptom, followed by cough (79.4%), convulsions (36.8%), and vomiting (23.2%). The multivariate logistic regression analysis showed that age (1-3 years old, odds ratio (OR): 3.193, 95% confidence interval (CI): 1.778-5.733], comorbidity (OR: 1.993, 95% CI:1.154-3.443), cough (OR: 0.409, 95% CI:0.236-0.709), and baseline neutrophil-to-lymphocyte ratio (OR: 1.108, 95% CI: 1.023-1.200), lactate dehydrogenase (OR: 1.993, 95% CI: 1.154-3.443), blood urea nitrogen (OR: 1.002, 95% CI: 1.000-1.003) and total bilirubin (OR: 1.178, 95% CI: 1.005-3.381) were independent risk factors for severe COVID-19. The area under the curve (AUC) of the prediction models constructed by multivariate logistic regression analysis and machine learning (RandomForest + TomekLinks) were 0.7770 and 0.8590, respectively. The top 10 most important variables of random forest variables were selected to build a prediction model, with an AUC of 0.8210. Compared with multivariate logistic regression, machine learning models could more accurately predict severe COVID-19 in children with Omicron variant infection.
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Affiliation(s)
- Pan Liu
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Zixuan Xing
- Department of Infectious Diseases, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaokang Peng
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Mengyi Zhang
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
| | - Chang Shu
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Ce Wang
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Ruina Li
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Li Tang
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Huijing Wei
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Xiaoshan Ran
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Sikai Qiu
- Department of Medicine, Xi'an Jiaotong University, Xi'an, China
| | - Ning Gao
- Department of Infectious Diseases, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yee Hui Yeo
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Xiaoguai Liu
- Department of Infectious Diseases, Xi'an Jiaotong University Affiliated Children's Hospital, Xi'an, Shaanxi, China
| | - Fanpu Ji
- Department of Infectious Diseases, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
- National & Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Shaanxi Provincial Clinical Medical Research Center of Infectious Diseases, Xi'an, China
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases, Xi'an Jiaotong University, Ministry of Education, Xi'an, China
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Feng Y, Yang L, Cheng Z, Li J. Derivation and external validation of a nomogram predicting the occurrence of severe illness among hospitalized coronavirus disease 2019 patients: a 2020 Chinese multicenter retrospective study. J Thorac Dis 2023; 15:6589-6603. [PMID: 38249879 PMCID: PMC10797405 DOI: 10.21037/jtd-23-653] [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: 04/19/2023] [Accepted: 11/03/2023] [Indexed: 01/23/2024]
Abstract
Background The worldwide pandemic of coronavirus disease 2019 (COVID-19) has still been an overwhelming public health challenge, and it is vital to identify determinants early to forecast the risk of severity using indicators easily available at admission. The current multicenter retrospective study aimed to derive and validate a user-friendly and effective nomogram to address this issue. Methods A training cohort consisting of 437 confirmed COVID-19 cases from three hospitals in Hubei province (Tongji Hospital affiliated with Huazhong University of Science and Technology, Wuhan Third Hospital of Wuhan University and Wuhan Jinyintan Hospital in Hubei province) was retrospectively analyzed to construct a predicting model, and another cohort of 161 hospitalized patients from Public Health Clinical Center of Shanghai was selected as an external validation cohort from January 1, 2020 to March 8, 2020. Determinants of developing into severe COVID-19 were probed using univariate regression together with a multivariate stepwise regression model. The risk of progression to severe COVID-19 was forecasted using the derived nomogram. The performances of the nomogram regarding the discrimination and calibration were assessed in the cohort of training as well as the cohort of external validation, respectively. Results A total of 144 (32.95%) and 54 (33.54%) patients, respectively, in cohorts of training and validation progressed to severe COVID-19 during hospitalization. Multivariable analyses showed determinants of severity consisted of hypertension, shortness of breath, platelet count, alanine aminotransferase (ALT), potassium, cardiac troponin I (cTnI), myohemoglobin, procalcitonin (PCT) and intervals from onset to diagnosis. The nomogram had good discrimination with concordance indices being 0.887 (95% CI: 0.854-0.919) and 0.850 (95% CI: 0.815-0.885) in internal and external validation, respectively. Calibration curves exhibited excellent concordance between the predictions by nomogram and actual observations in two cohorts. Conclusions We have established and validated an early predicting nomogram model, which can contribute to determine COVID-19 cases at risk of progression to severe illness.
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Affiliation(s)
- Yun Feng
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Luyu Yang
- Department of ICU/Emergency, Wuhan Third Hospital, Tongren Hospital of Wuhan University, Wuhan, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Li
- Clinical Research Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Tang SY, Chen TH, Kuo KL, Huang JN, Kuo CT, Chu YC. Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study. J Chin Med Assoc 2023; 86:1020-1027. [PMID: 37713313 DOI: 10.1097/jcma.0000000000000994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. METHODS A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts. RESULTS Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies. CONCLUSION This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.
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Affiliation(s)
- Shao-Yu Tang
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Tz-Heng Chen
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Ko-Lin Kuo
- Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taipei, Taiwan, ROC
| | - Jue-Ni Huang
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Chen-Tsung Kuo
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC
| | - Yuan-Chia Chu
- Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Big Data Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan, ROC
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Ahmadinejad N, Ayyoubzadeh SM, Zeinalkhani F, Delazar S, Javanmard Z, Ahmadinejad Z, Mohajeri A, Esmaeili M. Discovering associations between radiological features and COVID-19 patients' deterioration. Health Sci Rep 2023; 6:e1257. [PMID: 37711676 PMCID: PMC10497911 DOI: 10.1002/hsr2.1257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/17/2023] [Accepted: 04/23/2023] [Indexed: 09/16/2023] Open
Abstract
Background and Aims Data mining methods are effective and well-known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID-19 by applying the rule mining method using characteristics of medical images. Methods This retrospective study has analyzed the radiological data from 104 COVID-19 hospitalized patients diagnosed with COVID-19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes. Results Ten rules were extracted with only X-ray-related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan-related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13. Conclusion This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID-19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.
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Affiliation(s)
- Nasrin Ahmadinejad
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
- Radiology Department, Cancer Institute, Imam Khomeini Hospital ComplexTehran University of Medical ScienceTehranIran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
| | - Fahimeh Zeinalkhani
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
- Radiology Department, Cancer Institute, Imam Khomeini Hospital ComplexTehran University of Medical ScienceTehranIran
| | - Sina Delazar
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
| | - Zohreh Javanmard
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
| | - Zahra Ahmadinejad
- Department of Infectious Diseases, Imam Khomeini Hospital ComplexTehran University of Medical SciencesTehranIran
| | | | - Marzieh Esmaeili
- Department of Health Information Management, School of Allied Medical SciencesTehran University of Medical SciencesTehranIran
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Tulu TW, Wan TK, Chan CL, Wu CH, Woo PYM, Tseng CZS, Vodencarevic A, Menni C, Chan KHK. Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers. BMC DIGITAL HEALTH 2023; 1:6. [PMID: 38014372 PMCID: PMC9896457 DOI: 10.1186/s44247-022-00001-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 12/16/2022] [Indexed: 11/29/2023]
Abstract
COVID-19 mortality prediction Background COVID-19 has become a major global public health problem, despite prevention and efforts. The daily number of COVID-19 cases rapidly increases, and the time and financial costs associated with testing procedure are burdensome. Method To overcome this, we aim to identify immunological and metabolic biomarkers to predict COVID-19 mortality using a machine learning model. We included inpatients from Hong Kong's public hospitals between January 1, and September 30, 2020, who were diagnosed with COVID-19 using RT-PCR. We developed three machine learning models to predict the mortality of COVID-19 patients based on data in their electronic medical records. We performed statistical analysis to compare the trained machine learning models which are Deep Neural Networks (DNN), Random Forest Classifier (RF) and Support Vector Machine (SVM) using data from a cohort of 5,059 patients (median age = 46 years; 49.3% male) who had tested positive for COVID-19 based on electronic health records and data from 532,427 patients as controls. Result We identified top 20 immunological and metabolic biomarkers that can accurately predict the risk of mortality from COVID-19 with ROC-AUC of 0.98 (95% CI 0.96-0.98). Of the three models used, our result demonstrate that the random forest (RF) model achieved the most accurate prediction of mortality among COVID-19 patients with age, glomerular filtration, albumin, urea, procalcitonin, c-reactive protein, oxygen, bicarbonate, carbon dioxide, ferritin, glucose, erythrocytes, creatinine, lymphocytes, PH of blood and leukocytes among the most important biomarkers identified. A cohort from Kwong Wah Hospital (131 patients) was used for model validation with ROC-AUC of 0.90 (95% CI 0.84-0.92). Conclusion We recommend physicians closely monitor hematological, coagulation, cardiac, hepatic, renal and inflammatory factors for potential progression to severe conditions among COVID-19 patients. To the best of our knowledge, no previous research has identified important immunological and metabolic biomarkers to the extent demonstrated in our study. Supplementary Information The online version contains supplementary material available at 10.1186/s44247-022-00001-0.
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Affiliation(s)
- Thomas Wetere Tulu
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Computational Data Science Program, Addis Ababa University, Addis Ababa, Ethiopia
| | - Tsz Kin Wan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
| | - Ching Long Chan
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
| | - Chun Hei Wu
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
| | | | | | | | - Cristina Menni
- Department of Twin Research, King’s College London, London, UK
| | - Kei Hang Katie Chan
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
- Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI USA
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Tarasova O, Biziukova N, Shemshura A, Filimonov D, Kireev D, Pokrovskaya A, Poroikov VV. Identification of Molecular Mechanisms Involved in Viral Infection Progression Based on Text Mining: Case Study for HIV Infection. Int J Mol Sci 2023; 24:ijms24021465. [PMID: 36674980 PMCID: PMC9862153 DOI: 10.3390/ijms24021465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/29/2022] [Accepted: 01/06/2023] [Indexed: 01/13/2023] Open
Abstract
Viruses cause various infections that may affect human lifestyle for durations ranging from several days to for many years. Although preventative and therapeutic remedies are available for many viruses, they may still have a profound impact on human life. The human immunodeficiency virus type 1 is the most common cause of HIV infection, which represents one of the most dangerous and complex diseases since it affects the immune system and causes its disruption, leading to secondary complications and negatively influencing health-related quality of life. While highly active antiretroviral therapy may decrease the viral load and the velocity of HIV infection progression, some individual peculiarities may affect viral load control or the progression of T-cell malfunction induced by HIV. Our study is aimed at the text-based identification of molecular mechanisms that may be involved in viral infection progression, using HIV as a case study. Specifically, we identified human proteins and genes which commonly occurred, overexpressed or underexpressed, in the collections of publications relevant to (i) HIV infection progression and (ii) acute and chronic stages of HIV infection. Then, we considered biological processes that are controlled by the identified protein and genes. We verified the impact of the identified molecules in the associated clinical study.
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Affiliation(s)
- Olga Tarasova
- Institute of Biomedical Chemistry, 10 Bldg. 8, Pogodinskaya Str., 119121 Moscow, Russia
| | - Nadezhda Biziukova
- Institute of Biomedical Chemistry, 10 Bldg. 8, Pogodinskaya Str., 119121 Moscow, Russia
| | - Andrey Shemshura
- Federal Budget Public Health Institution “Clinical Center of HIV/AIDS Treatment and Prevention” of the Ministry of Health of Krasnodar Region, 204/2, im. Mitrofana Sedina Str., 350000 Krasnodar, Russia
| | - Dmitry Filimonov
- Institute of Biomedical Chemistry, 10 Bldg. 8, Pogodinskaya Str., 119121 Moscow, Russia
| | - Dmitry Kireev
- Federal Budget Institution of Science «Central Research Institute for Epidemiology» of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, Novogireevskaya Str., 3A, 111123 Moscow, Russia
| | - Anastasia Pokrovskaya
- Federal Budget Institution of Science «Central Research Institute for Epidemiology» of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing, Novogireevskaya Str., 3A, 111123 Moscow, Russia
- Department of Infectious Diseases with Courses of Epidemiology and Phthisiology, Medical Institute, Peoples’ Friendship University of Russia, 6 Miklukho-Maklaya Str., 117198 Moscow, Russia
| | - Vladimir V. Poroikov
- Institute of Biomedical Chemistry, 10 Bldg. 8, Pogodinskaya Str., 119121 Moscow, Russia
- Correspondence:
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10
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Moslehi S, Mahjub H, Farhadian M, Soltanian AR, Mamani M. Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran. BMC Med Res Methodol 2022; 22:339. [PMID: 36585627 PMCID: PMC9803600 DOI: 10.1186/s12874-022-01827-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients. METHODS This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models. RESULTS Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features. CONCLUSIONS Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.
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Affiliation(s)
- Samad Moslehi
- Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Hossein Mahjub
- Department of Biostatistics, School of Public Health, Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Maryam Farhadian
- Department of Biostatistics, School of Public Health, Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Ali Reza Soltanian
- Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Mojgan Mamani
- Brucellosis Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
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11
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Matysek A, Studnicka A, Smith WM, Hutny M, Gajewski P, Filipiak KJ, Goh J, Yang G. Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population. Front Med (Lausanne) 2022; 9:962101. [PMID: 35979209 PMCID: PMC9377050 DOI: 10.3389/fmed.2022.962101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection. Methods Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score. Results The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count. Conclusion The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.
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Affiliation(s)
- Adrian Matysek
- Immunidex Ltd., London, United Kingdom
- Cognescence Ltd., London, United Kingdom
| | - Aneta Studnicka
- Clinical Analysis Laboratory, Silesian Centre for Heart Diseases, Zabrze, Poland
| | - Wade Menpes Smith
- Immunidex Ltd., London, United Kingdom
- Cognescence Ltd., London, United Kingdom
| | - Michał Hutny
- Faculty of Medical Sciences in Katowice, Students’ Scientific Society, Medical University of Silesia, Katowice, Poland
| | - Paweł Gajewski
- AGH University of Science and Technology, Krakow, Poland
| | | | - Jorming Goh
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- National University Health System (NUHS), Centre for Healthy Longevity, Singapore, Singapore
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
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12
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Parviz M, Brieghel C, Agius R, Niemann CU. Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para)clinical data. Blood Adv 2022; 6:3716-3728. [PMID: 35468622 PMCID: PMC9631547 DOI: 10.1182/bloodadvances.2021006351] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 04/06/2022] [Indexed: 12/03/2022] Open
Abstract
A highly variable clinical course, immune dysfunction, and a complex genetic blueprint pose challenges for treatment decisions and the management of risk of infection in patients with chronic lymphocytic leukemia (CLL). In recent years, the use of machine learning (ML) technologies has made it possible to attempt to untangle such heterogeneous disease entities. In this study, using 3 classes of variables (international prognostic index for CLL [CLL-IPI] variables, baseline [para]clinical data, and data on recurrent gene mutations), we built ML predictive models to identify the individual risk of 4 clinical outcomes: death, treatment, infection, and the combined outcome of treatment or infection. Using the predictive models, we assessed to what extent the different classes of variables are predictive of the 4 different outcomes, within both a short-term 2-year outlook and a long-term 5-year outlook after CLL diagnosis. By adding the baseline (para)clinical data to CLL-IPI variables, predictive performance was improved, whereas no further improvement was observed when including the data on recurrent genetic mutations. We discovered 2 main clusters of variables predictive of treatment and infection. Further emphasizing the high mortality resulting from infection in CLL, we found a close similarity between variables predictive of infection in the short-term outlook and those predictive of death in the long-term outlook. We conclude that at the time of CLL diagnosis, routine (para)clinical data are more predictive of patient outcome than recurrent mutations. Future studies on modeling genetics and clinical outcome should always consider the inclusion of several (para)clinical data to improve performance.
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Affiliation(s)
- Mehdi Parviz
- Department of Hematology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; and
| | - Christian Brieghel
- Department of Hematology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Hematology, Zealand University Hospital, Roskilde Hospital, Roskilde, Denmark
| | - Rudi Agius
- Department of Hematology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Carsten U. Niemann
- Department of Hematology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; and
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Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/3783058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The COVID-19 pandemic has greatly affected populations worldwide and has posed a significant challenge to medical systems. With the constant increase in the number of severe COVID-19 infections, an essential area of research has been directed towards predicting the mortality rate of these patients, in order to make informed medical decisions about the necessary healthcare priorities. Although a large amount of research has attempted to predict the mortality rate of COVID-19 patients, the association between the mortality rate of COVID-19 patients and their underlying health conditions has been given significantly less attention. Meanwhile, patients with underlying conditions often face a worse COVID-19 prognosis. Therefore, the goal of this study was to classify the mortality rate of patients diagnosed with COVID-19, who also suffer from underlying health conditions or comorbidities. To achieve our goal, we applied machine learning (ML) models on a new publicly available dataset, not investigated by any existing literature. The dataset provides detailed information on 582 COVID-19 patients and facilitates a robust forecasting model of the mortality rate. The dataset was analysed using seven ML classifiers, namely, Bagging, J48, logistic regression (LR), random forest (RF), support vector machine (SVM), naïve Bayes (NB), and threshold selector. A comparative analysis was performed across the seven ML techniques, and their performance was assessed based on evaluation parameters including classification accuracy, true-positive rate, and false-positive rate. The best performance was demonstrated by the Bagging algorithm with an accuracy of 83.55% when using all the dataset features. The findings are intended to assist researchers and physicians in the early identification of at-risk COVID-19 patients and to make the appropriate intensive care decisions.
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Monday HN, Li J, Nneji GU, Nahar S, Hossin MA, Jackson J, Ejiyi CJ. COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network. Diagnostics (Basel) 2022; 12:diagnostics12030741. [PMID: 35328294 PMCID: PMC8946937 DOI: 10.3390/diagnostics12030741] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/07/2022] [Accepted: 03/15/2022] [Indexed: 02/04/2023] Open
Abstract
Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19 (COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis approach for COVID-19 classification based on CXR images could significantly reduce the clinician burden. There is no doubt that low resolution, noise and irrelevant annotations in chest X-ray images are a major constraint to the performance of AI-based COVID-19 diagnosis. While a few studies have made huge progress, they underestimate these bottlenecks. In this study, we propose a super-resolution-based Siamese wavelet multi-resolution convolutional neural network called COVID-SRWCNN for COVID-19 classification using chest X-ray images. Concretely, we first reconstruct high-resolution (HR) counterparts from low-resolution (LR) CXR images in order to enhance the quality of the dataset for improved performance of our model by proposing a novel enhanced fast super-resolution convolutional neural network (EFSRCNN) to capture texture details in each given chest X-ray image. Exploiting a mutual learning approach, the HR images are passed to the proposed Siamese wavelet multi-resolution convolutional neural network to learn the high-level features for COVID-19 classification. We validate the proposed COVID-SRWCNN model on public-source datasets, achieving accuracy of 98.98%. Our screening technique achieves 98.96% AUC, 99.78% sensitivity, 98.53% precision, and 98.86% specificity. Owing to the fact that COVID-19 chest X-ray datasets are low in quality, experimental results show that our proposed algorithm obtains up-to-date performance that is useful for COVID-19 screening.
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Affiliation(s)
- Happy Nkanta Monday
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Jianping Li
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
- Correspondence:
| | - Grace Ugochi Nneji
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (C.J.E.)
| | - Saifun Nahar
- Department of Information System and Technology, University of Missouri-St. Louis, St. Louis, MO 63121, USA;
| | - Md Altab Hossin
- School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Jehoiada Jackson
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (C.J.E.)
| | - Chukwuebuka Joseph Ejiyi
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; (G.U.N.); (J.J.); (C.J.E.)
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15
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Liu T, Siegel E, Shen D. Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction. Annu Rev Biomed Eng 2022; 24:179-201. [PMID: 35316609 DOI: 10.1146/annurev-bioeng-110220-012203] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in diagnosis, prediction, and management for COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Tianming Liu
- Department of Computer Science, University of Georgia, Athens, Georgia, USA;
| | - Eliot Siegel
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland, USA;
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China;
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16
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Zhang H, Zhong F, Wang B, Liao M. A nomogram predicting the severity of COVID-19 based on initial clinical and radiologic characteristics. Future Virol 2022. [PMID: 35371273 PMCID: PMC8862443 DOI: 10.2217/fvl-2020-0193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 01/24/2022] [Indexed: 12/15/2022]
Abstract
Aim: This study aimed to build an easy-to-use nomogram to predict the severity of COVID-19. Patients & methods: From December 2019 to January 2020, patients confirmed with COVID-19 in our hospital were enrolled. The initial clinical and radiological characteristics were extracted. Univariate and multivariate logistic regression were used to identify variables for the nomogram. Results: In total, 104 patients were included. Based on statistical analysis, age, levels of neutrophil count, creatinine, procalcitonin and numbers of involved lung segments were identified for nomogram. The area under the curve was 0.939 (95% CI: 0.893–0.984). The calibration curve showed good agreement between prediction of nomogram and observation in the primary cohort. Conclusion: An easy-to-use nomogram with great discrimination was built to predict the severity of COVID-19.
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Affiliation(s)
- Hanfei Zhang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Feiyang Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Binchen Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Meiyan Liao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
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Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L. Comparing different machine learning techniques for predicting COVID-19 severity. Infect Dis Poverty 2022; 11:19. [PMID: 35177120 PMCID: PMC8851750 DOI: 10.1186/s40249-022-00946-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 02/09/2022] [Indexed: 12/28/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is still ongoing spreading globally, machine learning techniques were used in disease diagnosis and to predict treatment outcomes, which showed favorable performance. The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest (RF), support vector machine (SVM), and logistic regression (LR). Feature importance to COVID-19 severity were further identified. Methods A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28, 2020, eighty-six demographic, clinical, and laboratory features were selected with LassoCV method, Spearman’s rank correlation, experts’ opinions, and literature evaluation. RF, SVM, and LR were performed to predict severe COVID-19, the performance of the models was compared by the area under curve (AUC). Additionally, feature importance to COVID-19 severity were analyzed by the best performance model. Results A total of 287 patients were enrolled with 36.6% severe cases and 63.4% non-severe cases. The median age was 60.0 years (interquartile range: 49.0–68.0 years). Three models were established using 23 features including 1 clinical, 1 chest computed tomography (CT) and 21 laboratory features. Among three models, RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928, RF also achieved a favorable sensitivity of 96.7%, specificity of 69.5%, and accuracy of 84.5%. SVM had sensitivity of 93.9%, specificity of 79.0%, and accuracy of 88.5%. LR also achieved a favorable sensitivity of 92.3%, specificity of 72.3%, and accuracy of 85.2%. Additionally, chest-CT had highest importance to illness severity, and the following features were neutrophil to lymphocyte ratio, lactate dehydrogenase, and D-dimer, respectively. Conclusions Our results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19, which may facilitate effective care and further optimize resources. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-00946-4.
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Affiliation(s)
- Yibai Xiong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China
| | - Yan Ma
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China
| | - Lianguo Ruan
- Department of Infectious Diseases, JinYinTan Hospital, Wuhan, 430040, China
| | - Dan Li
- Information Center, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Cheng Lu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China.
| | - Luqi Huang
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, No. 16, Nanxiao Street, Dongzhimen, Dongcheng District, Beijing, 100700, Beijing, China.
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Pezoulas VC, Kourou KD, Mylona E, Papaloukas C, Liontos A, Biros D, Milionis OI, Kyriakopoulos C, Kostikas K, Milionis H, Fotiadis DI. ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints. Comput Biol Med 2022; 141:105176. [PMID: 35007991 PMCID: PMC8711179 DOI: 10.1016/j.compbiomed.2021.105176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 01/08/2023]
Abstract
The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.
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Affiliation(s)
- Vasileios C Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - Konstantina D Kourou
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - Eugenia Mylona
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece
| | - Costas Papaloukas
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, GR45100, Greece
| | - Angelos Liontos
- Dept. of Internal Medicine, School of Medicine, University of Ioannina, Ioannina, GR45110, Greece
| | - Dimitrios Biros
- Dept. of Internal Medicine, School of Medicine, University of Ioannina, Ioannina, GR45110, Greece
| | - Orestis I Milionis
- Dept. of Internal Medicine, School of Medicine, University of Ioannina, Ioannina, GR45110, Greece
| | - Chris Kyriakopoulos
- Respiratory Medicine Dept., School of Medicine, University of Ioannina, Ioannina, GR45110, Greece
| | - Kostantinos Kostikas
- Respiratory Medicine Dept., School of Medicine, University of Ioannina, Ioannina, GR45110, Greece
| | - Haralampos Milionis
- Dept. of Internal Medicine, School of Medicine, University of Ioannina, Ioannina, GR45110, Greece
| | - Dimitrios I Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Institute of Biomedical Research, FORTH, Ioannina, GR45110, Greece.
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Pezoulas VC, Kourou KD, Papaloukas C, Triantafyllia V, Lampropoulou V, Siouti E, Papadaki M, Salagianni M, Koukaki E, Rovina N, Koutsoukou A, Andreakos E, Fotiadis DI. A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19. Diagnostics (Basel) 2021; 12:56. [PMID: 35054223 PMCID: PMC8774804 DOI: 10.3390/diagnostics12010056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 12/22/2021] [Accepted: 12/26/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU scoring index using dynamically associated biological markers. METHODS We propose a multimodal approach which combines explainable AI models with dynamic modeling methods to shed light into the clinical features of COVID-19. Dynamic Bayesian networks were used to seek associations among cytokines across four time intervals after hospitalization. Explainable gradient boosting trees were trained to predict the risk for ICU admission and mortality towards the development of an ICU scoring index. RESULTS Our results highlight LDH, IL-6, IL-8, Cr, number of monocytes, lymphocyte count, TNF as risk predictors for ICU admission and survival along with LDH, age, CRP, Cr, WBC, lymphocyte count for mortality in the ICU, with prediction accuracy 0.79 and 0.81, respectively. These risk factors were combined with dynamically associated biological markers to develop an ICU scoring index with accuracy 0.9. CONCLUSIONS to our knowledge, this is the first multimodal and explainable AI model which quantifies the risk of intensive care with accuracy up to 0.9 across multiple timepoints.
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Affiliation(s)
- Vasileios C. Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece; (V.C.P.); (K.D.K.); (C.P.)
| | - Konstantina D. Kourou
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece; (V.C.P.); (K.D.K.); (C.P.)
| | - Costas Papaloukas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece; (V.C.P.); (K.D.K.); (C.P.)
- Department of Biological Applications and Technology, University of Ioannina, GR45100 Ioannina, Greece
| | - Vassiliki Triantafyllia
- Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece; (V.T.); (V.L.); (E.S.); (M.P.); (M.S.); (E.A.)
| | - Vicky Lampropoulou
- Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece; (V.T.); (V.L.); (E.S.); (M.P.); (M.S.); (E.A.)
| | - Eleni Siouti
- Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece; (V.T.); (V.L.); (E.S.); (M.P.); (M.S.); (E.A.)
| | - Maria Papadaki
- Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece; (V.T.); (V.L.); (E.S.); (M.P.); (M.S.); (E.A.)
| | - Maria Salagianni
- Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece; (V.T.); (V.L.); (E.S.); (M.P.); (M.S.); (E.A.)
| | - Evangelia Koukaki
- Intensive Care Unit (ICU), 1st Department of Respiratory Medicine, Medical School, National and Kapodistrian University of Athens, ‘Sotiria’ General Hospital of Chest Diseases, GR11527 Athens, Greece; (E.K.); (N.R.); (A.K.)
| | - Nikoletta Rovina
- Intensive Care Unit (ICU), 1st Department of Respiratory Medicine, Medical School, National and Kapodistrian University of Athens, ‘Sotiria’ General Hospital of Chest Diseases, GR11527 Athens, Greece; (E.K.); (N.R.); (A.K.)
| | - Antonia Koutsoukou
- Intensive Care Unit (ICU), 1st Department of Respiratory Medicine, Medical School, National and Kapodistrian University of Athens, ‘Sotiria’ General Hospital of Chest Diseases, GR11527 Athens, Greece; (E.K.); (N.R.); (A.K.)
| | - Evangelos Andreakos
- Laboratory of Immunobiology, Center for Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, GR11527 Athens, Greece; (V.T.); (V.L.); (E.S.); (M.P.); (M.S.); (E.A.)
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece; (V.C.P.); (K.D.K.); (C.P.)
- Department of Biomedical Research, Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology (FORTH-IMBB), GR45110 Ioannina, Greece
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Banoei MM, Dinparastisaleh R, Zadeh AV, Mirsaeidi M. Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2021; 25:328. [PMID: 34496940 PMCID: PMC8424411 DOI: 10.1186/s13054-021-03749-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 08/27/2021] [Indexed: 01/08/2023]
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.
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Affiliation(s)
- Mohammad M Banoei
- Department of Critical Care Medicine, University of Calgary, Alberta, Canada.,Department of Biological Science, University of Calgary, Alberta, Canada
| | - Roshan Dinparastisaleh
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Ali Vaeli Zadeh
- Division of Pulmonary and Critical Care, Miami VA Medical Center, Miami, FL, USA
| | - Mehdi Mirsaeidi
- Division of Pulmonary and Critical Care, Department of Medicine, University of Miami, Miami, FL, USA.
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