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Peng T, Duong KS, Lu JY, Chacko KR, Henry S, Hou W, Fiori KP, Wang SH, Duong TQ. Incidence, characteristics, and risk factors of new liver disorders 3.5 years post COVID-19 pandemic in the Montefiore Health System in Bronx. PLoS One 2024; 19:e0303151. [PMID: 38870207 PMCID: PMC11175509 DOI: 10.1371/journal.pone.0303151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/20/2024] [Indexed: 06/15/2024] Open
Abstract
PURPOSE To determine the incidence of newly diagnosed liver disorders (LD) up to 3.5-year post-acute COVID-19, and risk factors associated with new LD. METHODS We analyzed 54,699 COVID-19 patients and 1,409,547 non-COVID-19 controls from March-11-2020 to Jan-03-2023. New liver disorders included abnormal liver function tests, advanced liver failure, alcohol and non-alcohol related liver disorders, and cirrhosis. Comparisons were made with ambulatory non-COVID-19 patients and patients hospitalized for other lower respiratory tract infections (LRTI). Demographics, comorbidities, laboratory data, incomes, insurance status, and unmet social needs were tabulated. The primary outcome was new LD at least two weeks following COVID-19 positive test. RESULTS Incidence of new LD was not significantly different between COVID-19 and non-COVID-19 cohorts (incidence:1.99% vs 1.90% p>0.05, OR = 1.04[95%CI: 0.92,1.17], p = 0.53). COVID-19 patients with new LD were older, more likely to be Hispanic and had higher prevalence of diabetes, hypertension, chronic kidney disease, and obesity compared to patients without new LD. Hospitalized COVID-19 patients had no elevated risk of LD compared to hospitalized LRTI patients (2.90% vs 2.07%, p>0.05, OR = 1.29[0.98,1.69], p = 0.06). Among COVID-19 patients, those who developed LD had fewer patients with higher incomes (14.18% vs 18.35%, p<0.05) and more with lower incomes (21.72% vs 17.23%, p<0.01), more Medicare and less Medicaid insurance, and more patients with >3 unmet social needs (6.49% vs 2.98%, p<0.001) and fewer with no unmet social needs (76.19% vs 80.42%, p<0.001). CONCLUSIONS Older age, Hispanic ethnicity, and obesity, but not COVID-19 status, posed increased risk for developing new LD. Lower socioeconomic status was associated with higher incidence of new LD.
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Affiliation(s)
- Thomas Peng
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Katie S. Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Justin Y. Lu
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Kristina R. Chacko
- Department of Medicine, Division of Hepatology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Sonya Henry
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Wei Hou
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Kevin P. Fiori
- Department of Pediatrics, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Stephen H. Wang
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
- Department of Surgery, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Tim Q. Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
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Lin YT, Lin KM, Wu KH, Lien F. Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score. BMC Med Inform Decis Mak 2024; 24:118. [PMID: 38702739 PMCID: PMC11069213 DOI: 10.1186/s12911-024-02523-1] [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: 11/18/2023] [Accepted: 04/29/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Pneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for prediction. We previously introduced the Blood Culture Prediction Index (BCPI) model, leveraging solely on complete blood count (CBC) and differential leukocyte count (DC), demonstrating its effectiveness in predicting bacteremia. Nevertheless, its potential in assessing pneumonia remains unexplored. Therefore, this study aims to compare the effectiveness of BCPI and CURB-65 in assessing pneumonia severity in an emergency department (ED) setting and develop an integrated ML model to enhance efficiency. METHODS This retrospective study was conducted at a 3400-bed tertiary medical center in Taiwan. Data from 9,352 patients with pneumonia in the ED between 2019 and 2021 were analyzed in this study. We utilized the BCPI model, which was trained on CBC/DC data, and computed CURB-65 scores for each patient to compare their prognosis prediction capabilities. Subsequently, we developed a novel Cox regression model to predict in-hospital mortality, integrating the BCPI model and CURB-65 scores, aiming to assess whether this integration enhances predictive performance. RESULTS The predictive performance of the BCPI model and CURB-65 score for the 30-day mortality rate in ED patients and the in-hospital mortality rate among admitted patients was comparable across all risk categories. However, the Cox regression model demonstrated an improved area under the ROC curve (AUC) of 0.713 than that of CURB-65 (0.668) for in-hospital mortality (p<0.001). In the lowest risk group (CURB-65=0), the Cox regression model outperformed CURB-65, with a significantly lower mortality rate (2.9% vs. 7.7%, p<0.001). CONCLUSIONS The BCPI model, constructed using CBC/DC data and ML techniques, performs comparably to the widely utilized CURB-65 in predicting outcomes for patients with pneumonia in the ED. Furthermore, by integrating the CURB-65 score and BCPI model into a Cox regression model, we demonstrated improved prediction capabilities, particularly for low-risk patients. Given its simple parameters and easy training process, the Cox regression model may be a more effective prediction tool for classifying patients with pneumonia in the emergency room.
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Affiliation(s)
- Yin-Ting Lin
- Department of Internal Medicine, Chang Gung Memorial Hospital, No. 6, W. Sec., Jiapu Rd., Puzih, Chiayi County, 613, Taiwan
| | - Ko-Ming Lin
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Chang Gung Memorial Hospital, No. 6, W. Sec., Jiapu Rd, Puzih, Chiayi County, 613, Taiwan
| | - Kai-Hsiang Wu
- Department of Emergency Medicine, Chang Gung Memorial Hospital, No. 6, W. Sec., Jiapu Rd., Puzih, Chiayi County, 613, Taiwan.
- Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi, Taiwan.
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Frank Lien
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Chang Gung Memorial Hospital, No. 6, W. Sec., Jiapu Rd, Puzih, Chiayi County, 613, Taiwan.
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Singh K, Kaur N, Prabhu A. Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review. Curr Top Med Chem 2024; 24:737-753. [PMID: 38318824 DOI: 10.2174/0115680266282179240124072121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak. PURPOSE The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development. METHODS A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax. RESULTS During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it. CONCLUSION We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.
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Affiliation(s)
- Kavya Singh
- Department of Biotechnology, Banasthali University, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India
| | - Navjeet Kaur
- Department of Chemistry & Division of Research and Development, Lovely Professional University, Phagwara, 144411, Punjab, India
| | - Ashish Prabhu
- Biotechnology Department, NIT Warangal, Warangal, 506004, Telangana, India
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Giuste FO, He L, Lais P, Shi W, Zhu Y, Hornback A, Tsai C, Isgut M, Anderson B, Wang MD. Early and fair COVID-19 outcome risk assessment using robust feature selection. Sci Rep 2023; 13:18981. [PMID: 37923795 PMCID: PMC10624921 DOI: 10.1038/s41598-023-36175-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 05/29/2023] [Indexed: 11/06/2023] Open
Abstract
Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.
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Affiliation(s)
- Felipe O Giuste
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Lawrence He
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Peter Lais
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Wenqi Shi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Yuanda Zhu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Andrew Hornback
- School of Computer Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Chiche Tsai
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Monica Isgut
- School of Biology, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Blake Anderson
- Department of Medicine, Emory University, Atlanta, GA, 30322, USA
| | - May D Wang
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA.
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Ghafoori M, Hamidi M, Modegh RG, Aziz-Ahari A, Heydari N, Tavafizadeh Z, Pournik O, Emdadi S, Samimi S, Mohseni A, Khaleghi M, Dashti H, Rabiee HR. Predicting survival of Iranian COVID-19 patients infected by various variants including omicron from CT Scan images and clinical data using deep neural networks. Heliyon 2023; 9:e21965. [PMID: 38058649 PMCID: PMC10696006 DOI: 10.1016/j.heliyon.2023.e21965] [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: 10/12/2022] [Revised: 10/26/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023] Open
Abstract
Purpose: The rapid spread of the COVID-19 omicron variant virus has resulted in an overload of hospitals around the globe. As a result, many patients are deprived of hospital facilities, increasing mortality rates. Therefore, mortality rates can be reduced by efficiently assigning facilities to higher-risk patients. Therefore, it is crucial to estimate patients' survival probability based on their conditions at the time of admission so that the minimum required facilities can be provided, allowing more opportunities to be available for those who need them. Although radiologic findings in chest computerized tomography scans show various patterns, considering the individual risk factors and other underlying diseases, it is difficult to predict patient prognosis through routine clinical or statistical analysis. Method: In this study, a deep neural network model is proposed for predicting survival based on simple clinical features, blood tests, axial computerized tomography scan images of lungs, and the patients' planned treatment. The model's architecture combines a Convolutional Neural Network and a Long Short Term Memory network. The model was trained using 390 survivors and 108 deceased patients from the Rasoul Akram Hospital and evaluated 109 surviving and 36 deceased patients infected by the omicron variant. Results: The proposed model reached an accuracy of 87.5% on the test data, indicating survival prediction possibility. The accuracy was significantly higher than the accuracy achieved by classical machine learning methods without considering computerized tomography scan images (p-value <= 4E-5). The images were also replaced with hand-crafted features related to the ratio of infected lung lobes used in classical machine-learning models. The highest-performing model reached an accuracy of 84.5%, which was considerably higher than the models trained on mere clinical information (p-value <= 0.006). However, the performance was still significantly less than the deep model (p-value <= 0.016). Conclusion: The proposed deep model achieved a higher accuracy than classical machine learning methods trained on features other than computerized tomography scan images. This proves the images contain extra information. Meanwhile, Artificial Intelligence methods with multimodal inputs can be more reliable and accurate than computerized tomography severity scores.
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Affiliation(s)
- Mahyar Ghafoori
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Mehrab Hamidi
- BCB Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Rassa Ghavami Modegh
- Data science and Machine learning Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- BCB Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Alireza Aziz-Ahari
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Neda Heydari
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Zeynab Tavafizadeh
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Omid Pournik
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Sasan Emdadi
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Saeed Samimi
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Amir Mohseni
- BCB Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Mohammadreza Khaleghi
- Radiology Department, Hazrat Rasoul Akram Hospital, School of Medicine, Iran University of Medical Sciences, Hemmat, Tehran, 14535, Iran
| | - Hamed Dashti
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
| | - Hamid R. Rabiee
- Data science and Machine learning Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- BCB Lab, Department of Computer Engineering, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
- AI-Med Group, AI Innovation Center, Sharif University of Technology, Azadi, Tehran, 11365-8639, Iran
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Datta D, George Dalmida S, Martinez L, Newman D, Hashemi J, Khoshgoftaar TM, Shorten C, Sareli C, Eckardt P. Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida. Front Digit Health 2023; 5:1193467. [PMID: 37588022 PMCID: PMC10426497 DOI: 10.3389/fdgth.2023.1193467] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/12/2023] [Indexed: 08/18/2023] Open
Abstract
Introduction The SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of COVID-19. This study aims to train an AI-driven decision support system that helps build a model to understand the most important features that predict the "mortality" of patients hospitalized with COVID-19. Methods We conducted a retrospective analysis of "5,371" patients hospitalized for COVID-19-related symptoms from the South Florida Memorial Health Care System between March 14th, 2020, and January 16th, 2021. A data set comprising patients' sociodemographic characteristics, pre-existing health information, and medication was analyzed. We trained Random Forest classifier to predict "mortality" for patients hospitalized with COVID-19. Results Based on the interpretability of the model, age emerged as the primary predictor of "mortality", followed by diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race in descending order of importance. Notably, individuals aged over 65 years (referred to as "older adults"), males, Whites, Hispanics, and current smokers were identified as being at higher risk of death. Additionally, BMI, specifically in the overweight and obese categories, significantly predicted "mortality". These findings indicated that the model effectively learned from various categories, such as patients' sociodemographic characteristics, pre-hospital comorbidities, and medications, with a predominant focus on characterizing pre-hospital comorbidities. Consequently, the model demonstrated the ability to predict "mortality" with transparency and reliability. Conclusion AI can potentially provide healthcare workers with the ability to stratify patients and streamline optimal care solutions when time is of the essence and resources are limited. This work sets the platform for future work that forecasts patient responses to treatments at various levels of disease severity and assesses health disparities and patient conditions that promote improved health care in a broader context. This study contributed to one of the first predictive analyses applying AI/ML techniques to COVID-19 data using a vast sample from South Florida.
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Affiliation(s)
- Debarshi Datta
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States
| | - Safiya George Dalmida
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States
| | - Laurie Martinez
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States
| | - David Newman
- Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States
| | - Javad Hashemi
- College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, United States
| | - Taghi M. Khoshgoftaar
- College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, United States
| | - Connor Shorten
- College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, United States
| | - Candice Sareli
- Memorial Healthcare System, Hollywood, FL, United States
| | - Paula Eckardt
- Memorial Healthcare System, Hollywood, FL, United States
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Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics (Basel) 2023; 13:1749. [PMID: 37238232 PMCID: PMC10217633 DOI: 10.3390/diagnostics13101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/29/2023] [Indexed: 05/28/2023] Open
Abstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
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Affiliation(s)
| | - Murat Koyuncu
- Department of Information Systems Engineering, Atilim University, 06830 Ankara, Turkey;
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Geelong, VIC 3216, Australia
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Shen B, Hou W, Jiang Z, Li H, Singer AJ, Hoshmand-Kochi M, Abbasi A, Glass S, Thode HC, Levsky J, Lipton M, Duong TQ. Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients. Diagnostics (Basel) 2023; 13:diagnostics13061107. [PMID: 36980414 PMCID: PMC10047384 DOI: 10.3390/diagnostics13061107] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors (N = 224) versus non-survivors (N = 28) in the general floor group, and (ii) survivors (N = 92) versus non-survivors (N = 56) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors (p < 0.05), and non-survivor CXR scores deteriorated at outcome (p < 0.05) whereas survivor CXR scores did not (p > 0.05). For IMV patients, survivor and non-survivor CXR scores were similar at intubation (p > 0.05), and both improved at outcome (p < 0.05), with survivor scores showing greater improvement (p < 0.05). Hospitalization and IMV duration were not different between groups (p > 0.05). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count (p < 0.05). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression.
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Affiliation(s)
- Beiyi Shen
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Wei Hou
- Department of Family Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Zhao Jiang
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Haifang Li
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Adam J. Singer
- Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mahsa Hoshmand-Kochi
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Almas Abbasi
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Samantha Glass
- Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Henry C. Thode
- Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Jeffrey Levsky
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA
| | - Michael Lipton
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA
| | - Tim Q. Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA
- Correspondence: ; Tel.: +718-920-6268
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Guarrasi V, Soda P. Multi-objective optimization determines when, which and how to fuse deep networks: An application to predict COVID-19 outcomes. Comput Biol Med 2023; 154:106625. [PMID: 36738713 PMCID: PMC9892294 DOI: 10.1016/j.compbiomed.2023.106625] [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: 10/28/2022] [Revised: 01/18/2023] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
The COVID-19 pandemic has caused millions of cases and deaths and the AI-related scientific community, after being involved with detecting COVID-19 signs in medical images, has been now directing the efforts towards the development of methods that can predict the progression of the disease. This task is multimodal by its very nature and, recently, baseline results achieved on the publicly available AIforCOVID dataset have shown that chest X-ray scans and clinical information are useful to identify patients at risk of severe outcomes. While deep learning has shown superior performance in several medical fields, in most of the cases it considers unimodal data only. In this respect, when, which and how to fuse the different modalities is an open challenge in multimodal deep learning. To cope with these three questions here we present a novel approach optimizing the setup of a multimodal end-to-end model. It exploits Pareto multi-objective optimization working with a performance metric and the diversity score of multiple candidate unimodal neural networks to be fused. We test our method on the AIforCOVID dataset, attaining state-of-the-art results, not only outperforming the baseline performance but also being robust to external validation. Moreover, exploiting XAI algorithms we figure out a hierarchy among the modalities and we extract the features' intra-modality importance, enriching the trust on the predictions made by the model.
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Affiliation(s)
- Valerio Guarrasi
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Italy.
| | - Paolo Soda
- Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå, University, Umeå, Sweden.
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10
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Lee HW, Yang HJ, Kim H, Kim UH, Kim DH, Yoon SH, Ham SY, Nam BD, Chae KJ, Lee D, Yoo JY, Bak SH, Kim JY, Kim JH, Kim KB, Jung JI, Lim JK, Lee JE, Chung MJ, Lee YK, Kim YS, Lee SM, Kwon W, Park CM, Kim YH, Jeong YJ, Jin KN, Goo JM. Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study. J Med Internet Res 2023; 25:e42717. [PMID: 36795468 PMCID: PMC9937110 DOI: 10.2196/42717] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/12/2022] [Accepted: 01/11/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. OBJECTIVE We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19. METHODS This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration. RESULTS The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859). CONCLUSIONS The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.
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Affiliation(s)
- Hyun Woo Lee
- Division of Respiratory and Critical Care, Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Hyun Jun Yang
- College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Hyungjin Kim
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Ue-Hwan Kim
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Dong Hyun Kim
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Soon Ho Yoon
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Soo-Youn Ham
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Bo Da Nam
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Republic of Korea
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine, Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Dabee Lee
- Department of Radiology, Dankook University Hospital, Cheonan, Republic of Korea
| | - Jin Young Yoo
- Department of Radiology, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - So Hyeon Bak
- Department of Radiology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Republic of Korea
| | - Jin Young Kim
- Department of Radiology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jin Hwan Kim
- Department of Radiology, Chungnam National University Hospital, College of Medicine, Daejeon, Republic of Korea
| | - Ki Beom Kim
- Department of Radiology, Daegu Fatima Hospital, Daegu, Republic of Korea
| | - Jung Im Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Kwang Lim
- Department of Radiology, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Jong Eun Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Myung Jin Chung
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Young Kyung Lee
- Department of Radiology, Seoul Medical Center, Seoul, Republic of Korea
| | - Young Seon Kim
- Department of Radiology, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, Republic of Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woocheol Kwon
- Department of Radiology, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea
| | - Chang Min Park
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Yun-Hyeon Kim
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Yeon Joo Jeong
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Biomedical Research Institute, Busan, Republic of Korea
| | - Kwang Nam Jin
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Jin Mo Goo
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Medical Research Center, Seoul, Republic of Korea
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Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. J Cardiovasc Dev Dis 2023; 10:jcdd10020039. [PMID: 36826535 PMCID: PMC9967447 DOI: 10.3390/jcdd10020039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/25/2023] Open
Abstract
Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.
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Velichko A, Huyut MT, Belyaev M, Izotov Y, Korzun D. Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207886. [PMID: 36298235 PMCID: PMC9610709 DOI: 10.3390/s22207886] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/10/2022] [Accepted: 10/14/2022] [Indexed: 05/16/2023]
Abstract
Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.
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Affiliation(s)
- Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
- Correspondence: (A.V.); (M.T.H.)
| | - Mehmet Tahir Huyut
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, 24000 Erzincan, Türkiye
- Correspondence: (A.V.); (M.T.H.)
| | - Maksim Belyaev
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Yuriy Izotov
- Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
| | - Dmitry Korzun
- Department of Computer Science, Institute of Mathematics and Information Technology, Petrozavodsk State University, 33 Lenin Ave., 185910 Petrozavodsk, Russia
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Duanmu H, Ren T, Li H, Mehta N, Singer AJ, Levsky JM, Lipton ML, Duong TQ. Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients. Biomed Eng Online 2022; 21:77. [PMID: 36242040 PMCID: PMC9568988 DOI: 10.1186/s12938-022-01045-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/16/2022] [Indexed: 11/10/2022] Open
Abstract
Objectives To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients. Methods This is a retrospective study. Serial pCXR and serial clinical variables were analyzed for data from day 1, day 5, day 1–3, day 3–5, or day 1–5 on IMV (110 IMV survivors and 76 IMV non-survivors). The outcome variables were duration on IMV and mortality. With fivefold cross-validation, the performance of the proposed deep learning system was evaluated by receiver operating characteristic (ROC) analysis and correlation analysis. Results Predictive models using 5-consecutive-day data outperformed those using 3-consecutive-day and 1-day data. Prediction using data closer to the outcome was generally better (i.e., day 5 data performed better than day 1 data, and day 3–5 data performed better than day 1–3 data). Prediction performance was generally better for the combined pCXR and non-imaging clinical data than either alone. The combined pCXR and non-imaging data of 5 consecutive days predicted mortality with an accuracy of 85 ± 3.5% (95% confidence interval (CI)) and an area under the curve (AUC) of 0.87 ± 0.05 (95% CI) and predicted the duration needed to be on IMV to within 2.56 ± 0.21 (95% CI) days on the validation dataset. Conclusions Deep learning of longitudinal pCXR and clinical data have the potential to accurately predict mortality and duration on IMV in COVID-19 patients. Longitudinal pCXR could have prognostic value if these findings can be validated in a large, multi-institutional cohort.
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Affiliation(s)
- Hongyi Duanmu
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Thomas Ren
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Haifang Li
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Neil Mehta
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Adam J Singer
- Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Jeffrey M Levsky
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Michael L Lipton
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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14
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Leiner J, Pellissier V, König S, Hohenstein S, Ueberham L, Nachtigall I, Meier-Hellmann A, Kuhlen R, Hindricks G, Bollmann A. Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network. Respir Res 2022; 23:264. [PMID: 36151525 PMCID: PMC9502925 DOI: 10.1186/s12931-022-02180-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 09/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach. METHODS Administrative data (dataset randomly split 75%/25% for model training/testing) from years 2016-2019 of 86 German Helios hospitals was retrospectively analyzed. Inpatient SARI cases were defined by ICD-codes J09-J22. Three ML algorithms were evaluated and its performance compared to generalized linear models (GLM) by computing receiver operating characteristic area under the curve (AUC) and area under the precision-recall curve (AUPRC). RESULTS The dataset contained 241,988 inpatient SARI cases (75 years or older: 49%; male 56.2%). In-hospital mortality was 11.6%. AUC and AUPRC in the testing dataset were 0.83 and 0.372 for GLM, 0.831 and 0.384 for random forest (RF), 0.834 and 0.382 for single layer neural network (NNET) and 0.834 and 0.389 for extreme gradient boosting (XGBoost). Statistical comparison of ROC AUCs revealed a better performance of NNET and XGBoost as compared to GLM. CONCLUSION ML algorithms for predicting in-hospital mortality were trained and tested on a large real-world administrative dataset of SARI patients and showed good discriminatory performances. Broad application of our models in clinical routine practice can contribute to patients' risk assessment and quality management.
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Affiliation(s)
- Johannes Leiner
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany. .,Real World Evidence and Health Technology Assessment, Helios Health Institute, Berlin, Germany.
| | - Vincent Pellissier
- Real World Evidence and Health Technology Assessment, Helios Health Institute, Berlin, Germany
| | - Sebastian König
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany.,Real World Evidence and Health Technology Assessment, Helios Health Institute, Berlin, Germany
| | - Sven Hohenstein
- Real World Evidence and Health Technology Assessment, Helios Health Institute, Berlin, Germany
| | - Laura Ueberham
- Clinic for Cardiology, University Hospital Leipzig, Leipzig, Germany
| | - Irit Nachtigall
- Department of Infectious Diseases and Infection Prevention, Helios Hospital Emil-von-Behring, Berlin, Germany.,Institute of Hygiene and Environmental Medicine, Charité - Universitaetsmedizin Berlin, Berlin, Germany
| | | | | | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany.,Real World Evidence and Health Technology Assessment, Helios Health Institute, Berlin, Germany
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15
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Gupta S, Shabaz M, Vyas S. Artificial intelligence and IoT based prediction of Covid-19 using chest X-ray images. SMART HEALTH 2022; 25:100299. [PMID: 35783463 PMCID: PMC9233885 DOI: 10.1016/j.smhl.2022.100299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/31/2022] [Accepted: 06/15/2022] [Indexed: 11/30/2022]
Abstract
Coronavirus illness (COVID-19), discovered in late 2019, has spread rapidly worldwide, resulting in significant mortality. This study analyzed the performance of studies that employed machines and DL on chest X-ray pictures and CT scans for COVID-19 diagnosis. ML approaches on CT and X-ray images aided incorrectly in identifying COVID-19. The fast spread of COVID-19 worldwide and the growing number of deaths necessitates an immediate response from all sectors. Authorities will be able to deal with the effects more efficiently if such illnesses can be predicted in the future. Furthermore, it is crucial to maintain track of the number of infected persons through regular check-ups, and it is frequently required to confine affected people and implement medical treatments. In addition, various additional elements, such as environmental influences and commonalities among the most afflicted places, should be considered to slow the spread of COVID-19, and precautions should be taken. AI-based approaches for the prediction and diagnosis of COVID-19 were suggested in this paper. This Review Article discusses current advances in AI technology and its biological applications, particularly the coronavirus.
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Palmisano A, Vignale D, Boccia E, Nonis A, Gnasso C, Leone R, Montagna M, Nicoletti V, Bianchi AG, Brusamolino S, Dorizza A, Moraschini M, Veettil R, Cereda A, Toselli M, Giannini F, Loffi M, Patelli G, Monello A, Iannopollo G, Ippolito D, Mancini EM, Pontone G, Vignali L, Scarnecchia E, Iannacone M, Baffoni L, Sperandio M, de Carlini CC, Sironi S, Rapezzi C, Antiga L, Jagher V, Di Serio C, Furlanello C, Tacchetti C, Esposito A. AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients. Radiol Med 2022; 127:960-972. [PMID: 36038790 PMCID: PMC9423702 DOI: 10.1007/s11547-022-01518-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/13/2022] [Indexed: 02/06/2023]
Abstract
Purpose To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification. Material and Methods In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766). Conclusions AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis. Supplementary Information The online version contains supplementary material available at 10.1007/s11547-022-01518-0.
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Affiliation(s)
- Anna Palmisano
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Davide Vignale
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Edda Boccia
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
| | - Alessandro Nonis
- Centro Universitario Di Statistica Per Le Scienze Biomediche, Vita-Salute San Raffaele University, Milan, Italy
| | - Chiara Gnasso
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Riccardo Leone
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Marco Montagna
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | - Valeria Nicoletti
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy
| | | | | | | | | | | | - Alberto Cereda
- GVM Care & Research Maria Cecilia Hospital, Cotignola, Italy
| | - Marco Toselli
- GVM Care & Research Maria Cecilia Hospital, Cotignola, Italy
| | | | | | | | | | | | | | | | | | | | - Elisa Scarnecchia
- ASST Valtellina and Alto Lario, Eugenio Morelli Hospital, Sondalo, Italy
| | - Mario Iannacone
- San Giovanni Bosco Hospital, ASL Città di Torino, Turin, Italy
| | - Lucio Baffoni
- Casa di Cura Villa dei Pini, Civitanova Marche, Italy
| | | | | | | | - Claudio Rapezzi
- GVM Care & Research Maria Cecilia Hospital, Cotignola, Italy
- Cardiologic Centre, University of Ferrara, Ferrara, Italy
| | | | | | - Clelia Di Serio
- Centro Universitario Di Statistica Per Le Scienze Biomediche, Vita-Salute San Raffaele University, Milan, Italy
| | | | - Carlo Tacchetti
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy.
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.
| | - Antonio Esposito
- Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy.
- School of Medicine, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy.
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Ortiz-Vilchis P, Ramirez-Arellano A. An Entropy-Based Measure of Complexity: An Application in Lung-Damage. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1119. [PMID: 36010783 PMCID: PMC9407132 DOI: 10.3390/e24081119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/23/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.
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Elshennawy NM, Ibrahim DM, Sarhan AM, Arafa M. Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19. Diagnostics (Basel) 2022; 12:1847. [PMID: 36010198 PMCID: PMC9406405 DOI: 10.3390/diagnostics12081847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 11/16/2022] Open
Abstract
The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients' mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a convolutional neural network (CNN); it is trained using a clinical dataset of 12,020 patients and is based on the 10-fold cross-validation (CV) approach for training and validation. The second predictive model, which we refer to as CV-LSTM + CNN, is developed by combining the long short-term memory (LSTM) approach with a CNN model. It is also trained using the clinical dataset based on the 10-fold CV approach for training and validation. The first two predictive models use the clinical dataset in its original CSV form. The last one, which we refer to as IMG-CNN, is a CNN model and is trained alternatively using the converted images of the clinical dataset, where each image corresponds to a data row from the original clinical dataset. The experimental results revealed that the IMG-CNN predictive model outperforms the other two with an average accuracy of 94.14%, a precision of 100%, a recall of 91.0%, a specificity of 100%, an F1-score of 95.3%, an AUC of 93.6%, and a loss of 0.22.
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Affiliation(s)
- Nada M. Elshennawy
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
| | - Dina M. Ibrahim
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Amany M. Sarhan
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
| | - Mohamed Arafa
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt; (D.M.I.); (A.M.S.); (M.A.)
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Kim KM, Evans DS, Jacobson J, Jiang X, Browner W, Cummings SR. Rapid prediction of in-hospital mortality among adults with COVID-19 disease. PLoS One 2022; 17:e0269813. [PMID: 35905072 PMCID: PMC9337639 DOI: 10.1371/journal.pone.0269813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 05/29/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND We developed a simple tool to estimate the probability of dying from acute COVID-19 illness only with readily available assessments at initial admission. METHODS This retrospective study included 13,190 racially and ethnically diverse adults admitted to one of the New York City Health + Hospitals (NYC H+H) system for COVID-19 illness between March 1 and June 30, 2020. Demographic characteristics, simple vital signs and routine clinical laboratory tests were collected from the electronic medical records. A clinical prediction model to estimate the risk of dying during the hospitalization were developed. RESULTS Mean age (interquartile range) was 58 (45-72) years; 5421 (41%) were women, 5258 were Latinx (40%), 3805 Black (29%), 1168 White (9%), and 2959 Other (22%). During hospitalization, 2,875 were (22%) died. Using separate test and validation samples, machine learning (Gradient Boosted Decision Trees) identified eight variables-oxygen saturation, respiratory rate, systolic and diastolic blood pressures, pulse rate, blood urea nitrogen level, age and creatinine-that predicted mortality, with an area under the ROC curve (AUC) of 94%. A score based on these variables classified 5,677 (46%) as low risk (a score of 0) who had 0.8% (95% confidence interval, 0.5-1.0%) risk of dying, and 674 (5.4%) as high-risk (score ≥ 12 points) who had a 97.6% (96.5-98.8%) risk of dying; the remainder had intermediate risks. A risk calculator is available online at https://danielevanslab.shinyapps.io/Covid_mortality/. CONCLUSIONS In a diverse population of hospitalized patients with COVID-19 illness, a clinical prediction model using a few readily available vital signs reflecting the severity of disease may precisely predict in-hospital mortality in diverse populations and can rapidly assist decisions to prioritize admissions and intensive care.
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Affiliation(s)
- Kyoung Min Kim
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Sutter Health, San Francisco, California, United States of America
- Division of Endocrinology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea
| | - Daniel S. Evans
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Sutter Health, San Francisco, California, United States of America
| | - Jessica Jacobson
- New York City Health + Hospitals/Bellevue-NYU Grossman School of Medicine, New York, New York, United States of America
| | - Xiaqing Jiang
- Orthopedic Surgery, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Warren Browner
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Sutter Health, San Francisco, California, United States of America
| | - Steven R. Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, Sutter Health, San Francisco, California, United States of America
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
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20
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Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques. Diagnostics (Basel) 2022; 12:diagnostics12061396. [PMID: 35741207 PMCID: PMC9222115 DOI: 10.3390/diagnostics12061396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 05/28/2022] [Accepted: 05/30/2022] [Indexed: 11/17/2022] Open
Abstract
The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.
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21
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Wang D, Willis DR, Yih Y. The pneumonia severity index: Assessment and comparison to popular machine learning classifiers. Int J Med Inform 2022; 163:104778. [PMID: 35487075 DOI: 10.1016/j.ijmedinf.2022.104778] [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: 01/13/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size. METHODS In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34,720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC). RESULTS Machine learning classifiers, such as Random Forest, provided a statistically highly(p < 0.001) significant improvement (∼33% in PR AUC and ∼6% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI). DISCUSSION Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.
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Affiliation(s)
- Dawei Wang
- School of Industrial Engineering, Purdue University, 315 Grant St, West Lafayette, IN 47907, USA.
| | - Deanna R Willis
- Indiana University School of Medicine, Department of Family Medicine, 1110 W. Michigan St, LO 200, Indianapolis, IN 46202, USA
| | - Yuehwern Yih
- School of Industrial Engineering, Purdue University, 315 Grant St, West Lafayette, IN 47907, USA
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22
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Reina Reina A, Barrera JM, Valdivieso B, Gas ME, Maté A, Trujillo JC. Machine learning model from a Spanish cohort for prediction of SARS-COV-2 mortality risk and critical patients. Sci Rep 2022; 12:5723. [PMID: 35388055 PMCID: PMC8986770 DOI: 10.1038/s41598-022-09613-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 03/24/2022] [Indexed: 12/11/2022] Open
Abstract
Patients affected by SARS-COV-2 have collapsed healthcare systems around the world. Consequently, different challenges arise regarding the prediction of hospital needs, optimization of resources, diagnostic triage tools and patient evolution, as well as tools that allow us to analyze which are the factors that determine the severity of patients. Currently, it is widely accepted that one of the problems since the pandemic appeared was to detect (i) who patients were about to need Intensive Care Unit (ICU) and (ii) who ones were about not overcome the disease. These critical patients collapsed Hospitals to the point that many surgeries around the world had to be cancelled. Therefore, the aim of this paper is to provide a Machine Learning (ML) model that helps us to prevent when a patient is about to be critical. Although we are in the era of data, regarding the SARS-COV-2 patients, there are currently few tools and solutions that help medical professionals to predict the evolution of patients in order to improve their treatment and the needs of critical resources at hospitals. Moreover, most of these tools have been created from small populations and/or Chinese populations, which carries a high risk of bias. In this paper, we present a model, based on ML techniques, based on 5378 Spanish patients’ data from which a quality cohort of 1201 was extracted to train the model. Our model is capable of predicting the probability of death of patients with SARS-COV-2 based on age, sex and comorbidities of the patient. It also allows what-if analysis, with the inclusion of comorbidities that the patient may develop during the SARS-COV-2 infection. For the training of the model, we have followed an agnostic approach. We explored all the active comorbidities during the SARS-COV-2 infection of the patients with the objective that the model weights the effect of each comorbidity on the patient’s evolution according to the data available. The model has been validated by using stratified cross-validation with k = 5 to prevent class imbalance. We obtained robust results, presenting a high hit rate, with 84.16% accuracy, 83.33% sensitivity, and an Area Under the Curve (AUC) of 0.871. The main advantage of our model, in addition to its high success rate, is that it can be used with medical records in order to predict their diagnosis, allowing the critical population to be identified in advance. Furthermore, it uses the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) standard. In this sense, we should also emphasize that those hospitals using other encodings can add an intermediate layer business to business (B2B) with the aim of making transformations to the same international format.
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Affiliation(s)
- Alejandro Reina Reina
- Lucentia Department of Software and Computing Systems, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain. .,Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain.
| | - José M Barrera
- Lucentia Department of Software and Computing Systems, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain.,Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - Bernardo Valdivieso
- The University and Polytechnic La Fe Hospital of Valencia, Avenida Fernando Abril Martorell, 106 Torre H 7a planta, 46026, Valencia, Spain
| | - María-Eugenia Gas
- The University and Polytechnic La Fe Hospital of Valencia, Avenida Fernando Abril Martorell, 106 Torre H 7a planta, 46026, Valencia, Spain
| | - Alejandro Maté
- Lucentia Department of Software and Computing Systems, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain.,Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
| | - Juan C Trujillo
- Lucentia Department of Software and Computing Systems, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain.,Lucentia Lab, Av. Pintor Pérez Gil, 16, 03540, Alicante, Spain
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23
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Lu JY, Buczek A, Fleysher R, Hoogenboom WS, Hou W, Rodriguez CJ, Fisher MC, Duong TQ. Outcomes of Hospitalized Patients With COVID-19 With Acute Kidney Injury and Acute Cardiac Injury. Front Cardiovasc Med 2022; 8:798897. [PMID: 35242818 PMCID: PMC8886161 DOI: 10.3389/fcvm.2021.798897] [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: 10/20/2021] [Accepted: 12/27/2021] [Indexed: 01/09/2023] Open
Abstract
Purpose This study investigated the incidence, disease course, risk factors, and mortality in COVID-19 patients who developed both acute kidney injury (AKI) and acute cardiac injury (ACI), and compared to those with AKI only, ACI only, and no injury (NI). Methods This retrospective study consisted of hospitalized COVID-19 patients at Montefiore Health System in Bronx, New York between March 11, 2020 and January 29, 2021. Demographics, comorbidities, vitals, and laboratory tests were collected during hospitalization. Predictive models were used to predict AKI, ACI, and AKI-ACI onset. Longitudinal laboratory tests were analyzed with time-lock to discharge alive or death. Results Of the 5,896 hospitalized COVID-19 patients, 44, 19, 9, and 28% had NI, AKI, ACI, and AKI-ACI, respectively. Most ACI presented very early (within a day or two) during hospitalization in contrast to AKI (p < 0.05). Patients with combined AKI-ACI were significantly older, more often men and had more comorbidities, and higher levels of cardiac, kidney, liver, inflammatory, and immunological markers compared to those of the AKI, ACI, and NI groups. The adjusted hospital-mortality odds ratios were 17.1 [95% CI = 13.6–21.7, p < 0.001], 7.2 [95% CI = 5.4–9.6, p < 0.001], and 4.7 [95% CI = 3.7–6.1, p < 0.001] for AKI-ACI, ACI, and AKI, respectively, relative to NI. A predictive model of AKI-ACI onset using top predictors yielded 97% accuracy. Longitudinal laboratory data predicted mortality of AKI-ACI patients up to 5 days prior to outcome, with an area-under-the-curve, ranging from 0.68 to 0.89. Conclusions COVID-19 patients with AKI-ACI had markedly worse outcomes compared to those only AKI, ACI and NI. Common laboratory variables accurately predicted AKI-ACI. The ability to identify patients at risk for AKI-ACI could lead to earlier intervention and improvement in clinical outcomes.
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Affiliation(s)
- Justin Y Lu
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Alexandra Buczek
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Roman Fleysher
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Wouter S Hoogenboom
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Wei Hou
- Department of Family, Population and Preventive Medicine, Stony Brook Medicine, New York, NY, United States
| | - Carlos J Rodriguez
- Cardiology Division, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Molly C Fisher
- Nephrology Division, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
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24
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Elghamrawy SM, Hassanien AE, Vasilakos AV. Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022; 32:614-628. [PMID: 34518740 PMCID: PMC8426801 DOI: 10.1002/ima.22644] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 06/13/2023]
Abstract
The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID-19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic-based adaptive momentum estimation (GB-ADAM) algorithm. The GB-ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8 + T Lymphocyte (Count), D-dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C-reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID-19 signs in CT scans included ground-glass opacity (GGO), followed by crazy-paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models.
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Affiliation(s)
| | | | - Athanasios V. Vasilakos
- Department of Computer ScienceLulea University of Technology (LTU)Sweden
- Department of Electrical and Data EngineeringUniversity of Technology Sydney (UTS)Australia
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25
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COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data. Eur Radiol 2022; 32:4446-4456. [PMID: 35184218 PMCID: PMC8857913 DOI: 10.1007/s00330-022-08588-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/14/2021] [Accepted: 01/22/2022] [Indexed: 01/19/2023]
Abstract
Objectives We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). Methods Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. Results A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. Conclusions The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. Key Points • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08588-8.
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26
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Lu JQ, Lu JY, Wang W, Liu Y, Buczek A, Fleysher R, Hoogenboom WS, Zhu W, Hou W, Rodriguez CJ, Duong TQ. Clinical predictors of acute cardiac injury and normalization of troponin after hospital discharge from COVID-19. EBioMedicine 2022; 76:103821. [PMID: 35144887 PMCID: PMC8819639 DOI: 10.1016/j.ebiom.2022.103821] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 12/15/2022] Open
Abstract
Background Although acute cardiac injury (ACI) is a known COVID-19 complication, whether ACI acquired during COVID-19 recovers is unknown. This study investigated the incidence of persistent ACI and identified clinical predictors of ACI recovery in hospitalized patients with COVID-19 2.5 months post-discharge. Methods This retrospective study consisted of 10,696 hospitalized COVID-19 patients from March 11, 2020 to June 3, 2021. Demographics, comorbidities, and laboratory tests were collected at ACI onset, hospital discharge, and 2.5 months post-discharge. ACI was defined as serum troponin-T (TNT) level >99th-percentile upper reference limit (0.014ng/mL) during hospitalization, and recovery was defined as TNT below this threshold 2.5 months post-discharge. Four models were used to predict ACI recovery status. Results There were 4,248 (39.7%) COVID-19 patients with ACI, with most (93%) developed ACI on or within a day after admission. In-hospital mortality odds ratio of ACI patients was 4.45 [95%CI: 3.92, 5.05, p<0.001] compared to non-ACI patients. Of the 2,880 ACI survivors, 1,114 (38.7%) returned to our hospitals 2.5 months on average post-discharge, of which only 302 (44.9%) out of 673 patients recovered from ACI. There were no significant differences in demographics, race, ethnicity, major commodities, and length of hospital stay between groups. Prediction of ACI recovery post-discharge using the top predictors (troponin, creatinine, lymphocyte, sodium, lactate dehydrogenase, lymphocytes and hematocrit) at discharge yielded 63.73%-75.73% accuracy. Interpretation Persistent cardiac injury is common among COVID-19 survivors. Readily available patient data accurately predict ACI recovery post-discharge. Early identification of at-risk patients could help prevent long-term cardiovascular complications. Funding None
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Affiliation(s)
- Joyce Q Lu
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States
| | - Justin Y Lu
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States
| | - Weihao Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States
| | - Yuhang Liu
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States
| | - Alexandra Buczek
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States
| | - Roman Fleysher
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States
| | - Wouter S Hoogenboom
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States
| | - Wei Zhu
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, United States
| | - Wei Hou
- Department of Family and Preventive Medicine, Stony Brook University, Stony Brook, New York, United States
| | - Carlos J Rodriguez
- Department of Medicine, Cardiology Division, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States.
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Lu JY, Hou W, Duong TQ. Longitudinal prediction of hospital-acquired acute kidney injury in COVID-19: a two-center study. Infection 2022; 50:109-119. [PMID: 34176087 PMCID: PMC8235913 DOI: 10.1007/s15010-021-01646-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 06/20/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND To investigate the temporal characteristics of clinical variables of hospital-acquired acute kidney injury (AKI) in COVID-19 patients and to longitudinally predict AKI onset. METHODS There were 308 hospital-acquired AKI and 721 non-AKI (NAKI) COVID-19 patients from Stony Brook Hospital (New York, USA) data, and 72 hospital-acquired AKI and 303 NAKI COVID-19 patients from Tongji Hospital (Wuhan, China). Demographic, comorbidities, and longitudinal (3 days before and 3 days after AKI onset) clinical variables were used to compute odds ratios for and longitudinally predict hospital-acquired AKI onset. RESULTS COVID-19 patients with AKI were more likely to die than NAKI patients (31.5% vs 6.9%, adjusted p < 0.001, OR = 4.67 [95% CI 3.1, 7.0], Stony Brook data). AKI developed on average 3.3 days after hospitalization. Procalcitonin was elevated prior to AKI onset (p < 0.05), peaked, and remained elevated (p < 0.05). Alanine aminotransferase, aspartate transaminase, ferritin, and lactate dehydrogenase peaked the same time as creatinine, whereas D-dimer and brain natriuretic peptide peaked a day later. C-reactive protein, white blood cell and lymphocyte showed group differences - 2 days prior (p < 0.05). Top predictors were creatinine, procalcitonin, white blood cells, lactate dehydrogenase, and lymphocytes. They predicted AKI onset with areas under curves (AUCs) of 0.78, 0.66, and 0.56 at 0, - 1, and - 2 days prior, respectively. When tested on the Tongji Hospital data, the AUCs were 0.80, 0.79, and 0.77, respectively. CONCLUSIONS Time-locked longitudinal data provide insight into AKI progression. Commonly clinical variables reasonably predict AKI onset a few days prior. This work may lead to earlier recognition of AKI and treatment to improve clinical outcomes.
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Affiliation(s)
- Justin Y. Lu
- grid.251993.50000000121791997Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY 10467 USA
| | - Wei Hou
- grid.459987.e0000 0004 6008 5093Department of Family, Population & Preventive Medicine, Stony Brook Medicine, 101 Nicolls Rd, Stony Brook, NY USA
| | - Tim Q. Duong
- grid.251993.50000000121791997Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY 10467 USA
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28
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Ferreira Junior JR. Time-to-event assessment for the discovery of the proper prognostic value of clinical biomarkers optimized for COVID-19. Clinics (Sao Paulo) 2022; 77:100009. [PMID: 35405433 PMCID: PMC8813569 DOI: 10.1016/j.clinsp.2022.100009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/14/2021] [Indexed: 12/14/2022] Open
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Sarker S, Jamal L, Ahmed SF, Irtisam N. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. ROBOTICS AND AUTONOMOUS SYSTEMS 2021; 146:103902. [PMID: 34629751 PMCID: PMC8493645 DOI: 10.1016/j.robot.2021.103902] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/03/2021] [Accepted: 09/13/2021] [Indexed: 05/05/2023]
Abstract
The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.
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Affiliation(s)
- Sujan Sarker
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Lafifa Jamal
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Syeda Faiza Ahmed
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Niloy Irtisam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
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Musheyev B, Janowicz R, Borg L, Matarlo M, Boyle H, Hou W, Duong TQ. Characterizing non-critically ill COVID-19 survivors with and without in-hospital rehabilitation. Sci Rep 2021; 11:21039. [PMID: 34702883 PMCID: PMC8548441 DOI: 10.1038/s41598-021-00246-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 10/05/2021] [Indexed: 12/16/2022] Open
Abstract
This study investigated pre-COVID-19 admission dependency, discharge assistive equipment, discharge medical follow-up recommendation, and functional status at hospital discharge of non-critically ill COVID-19 survivors, stratified by those with (N = 155) and without (N = 162) in-hospital rehabilitation. “Mental Status”, intensive-care-unit (ICU) Mobility, and modified Barthel Index scores were assessed at hospital discharge. Relative to the non-rehabilitation patients, rehabilitation patients were older, had more comorbidities, worse pre-admission dependency, were discharged with more assistive equipment and supplemental oxygen, spent more days in the hospital, and had more hospital-acquired acute kidney injury, acute respiratory failure, and more follow-up referrals (p < 0.05 for all). Cardiology, vascular medicine, urology, and endocrinology were amongst the top referrals. Functional scores of many non-critically ill COVID-19 survivors were abnormal at discharge (p < 0.05) and were associated with pre-admission dependency (p < 0.05). Some functional scores were negatively correlated with age, hypertension, coronary artery disease, chronic kidney disease, psychiatric disease, anemia, and neurological disorders (p < 0.05). In-hospital rehabilitation providing restorative therapies and assisting discharge planning were challenging in COVID-19 circumstances. Knowledge of the functional status, discharge assistive equipment, and follow-up medical recommendations at discharge could enable appropriate and timely post-discharge care. Follow-up studies of COVID-19 survivors are warranted as many will likely have significant post-acute COVID-19 sequela.
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Affiliation(s)
- Benjamin Musheyev
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA.,Renaissance School of Medicine at Stony Brook University, Stony Brook University, Stony Brook, NY, USA
| | - Rebeca Janowicz
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook Medicine, Stony Brook, NY, USA
| | - Lara Borg
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook Medicine, Stony Brook, NY, USA
| | - Michael Matarlo
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook Medicine, Stony Brook, NY, USA
| | - Hayle Boyle
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook Medicine, Stony Brook, NY, USA
| | - Wei Hou
- Department of Family, Population and Preventative Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA.
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA.
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31
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Naseem M, Arshad H, Hashmi SA, Irfan F, Ahmed FS. Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network. Int J Med Inform 2021; 154:104556. [PMID: 34455118 PMCID: PMC8378987 DOI: 10.1016/j.ijmedinf.2021.104556] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/03/2021] [Accepted: 08/15/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND The nextwave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a novel machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. METHODS We collected clinical and laboratory data prospectively on all adult patients (≥18 years of age) that were admitted in the inpatient setting at Aga Khan University Hospital between February 2020 and September 2020 with a clinical diagnosis of COVID-19 infection. Only patients with a RT-PCR (reverse polymerase chain reaction) proven COVID-19 infection and complete medical records were included in this study. A Novel 3-phase machine learning framework was developed to predict mortality in the inpatients setting. Phase 1 included variable selection that was done using univariate and multivariate Cox-regression analysis; all variables that failed the regression analysis were excluded from the machine learning phase of the study. Phase 2 involved new-variables creation and selection. Phase 3 and final phase applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models, etc. The accuracy of these models were evaluated using test-set accuracy, sensitivity, specificity, positive predictive values, negative predictive values and area under the receiver-operating curves. RESULTS After application of inclusion and exclusion criteria (n=)1214 patients were selected from a total of 1228 admitted patients. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our deep neural network (DNN) (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the receiver-operator curve of 88.5. CONCLUSION Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.
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Affiliation(s)
- Maleeha Naseem
- Department of Community Health Sciences, Aga Khan University, Karachi 74900, Pakistan
| | - Hajra Arshad
- Medical College, Aga Khan University, Karachi 74900, Pakistan
| | | | - Furqan Irfan
- College of Osteopathic Medicine, Institute of Global Health, Michigan State University, East Lansing, MI 48824, United States
| | - Fahad Shabbir Ahmed
- Clinicaro Machine Learning Group, New Haven, CT 06510, United States,Department of Pathology, Wayne State University, Detroit, MI 48201, United States,Corresponding author at: Department of Pathology, Wayne State University, Detroit, MI 48201, United States
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32
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Yildiz H, Castanares-Zapatero D, Pierman G, Pothen L, De Greef J, Aboubakar Nana F, Rodriguez-Villalobos H, Belkhir L, Yombi JC. Validation of Neutrophil-to-Lymphocyte Ratio Cut-off Value Associated with High In-Hospital Mortality in COVID-19 Patients. Int J Gen Med 2021; 14:5111-5117. [PMID: 34511993 PMCID: PMC8420786 DOI: 10.2147/ijgm.s326666] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 08/20/2021] [Indexed: 12/18/2022] Open
Abstract
Introduction The neutrophil-to-lymphocyte ratio (NLR) could be a predictive factor of severe COVID-19. However, most relevant studies are retrospective, and the optimal NLR cut-off point has not been determined. The objective of our research was identification and validation of the best NLR cut-off value on admission that could predict high in-hospital mortality in COVID-19 patients. Methods Medical files of all patients admitted for COVID-19 pneumonia in our dedicated COVID-units between March and April 2020 (derivation cohort) and between October and December 2020 (validation cohort) were reviewed. Results Two hundred ninety-nine patients were included in the study (198 in the derivation and 101 in the validation cohort, respectively). Youden’s J statistic in the derivation cohort determined the optimal cut-off value for the performance of NLR at admission to predict mortality in hospitalized patients with COVID-19. The NLR cut-off value of 5.94 had a sensitivity of 62% and specificity of 64%. In ROC curve analysis, the AUC was 0.665 [95% CI 0.530–0.801, p= 0.025]. In the validation cohort, the best predictive cut-off value of NLR was 6.4, which corresponded to a sensitivity of 63% and a specificity of 64% with AUC 0.766 [95% CI 0.651–0.881, p <0.001]. When the NLR cut-off value of 5.94 was applied in the validation cohort, there was no significant difference in death and survival in comparison with the derivation NLR cut-off. Net reclassification improvement (NRI) analysis showed no significant classification change in outcome between both NLR cut-off values (NRI:0.012, p=0.31). Conclusion In prospective analysis, an NLR value of 5.94 predicted high in-hospital mortality upon admission in patients hospitalized for COVID-19 pneumonia.
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Affiliation(s)
- Halil Yildiz
- Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | | | - Guillaume Pierman
- Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Lucie Pothen
- Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Julien De Greef
- Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Frank Aboubakar Nana
- Department of Pneumology, Cliniques Universitaires Saint-Luc, UCLouvain, Brussels, Belgium
| | | | - Leila Belkhir
- Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Jean Cyr Yombi
- Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
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Bottino F, Tagliente E, Pasquini L, Napoli AD, Lucignani M, Figà-Talamanca L, Napolitano A. COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal. J Pers Med 2021; 11:893. [PMID: 34575670 PMCID: PMC8467935 DOI: 10.3390/jpm11090893] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 08/26/2021] [Accepted: 09/03/2021] [Indexed: 12/21/2022] Open
Abstract
More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.
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Affiliation(s)
- Francesca Bottino
- Medical Physics Department Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
| | - Emanuela Tagliente
- Medical Physics Department Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, 00165 Rome, Italy; (L.P.); (A.D.N.)
- Neuroradiology Service, Radiology Department, Memorial Sloan Kettering Cancer Center, New York, NY 1275, USA
| | - Alberto Di Napoli
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, 00165 Rome, Italy; (L.P.); (A.D.N.)
- Radiology Department, Castelli Romani Hospital, 00040 Ariccia (RM), Italy
| | - Martina Lucignani
- Medical Physics Department Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
| | - Antonio Napolitano
- Medical Physics Department Bambino Gesù Children’s Hospital, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS), 00165 Rome, Italy;
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Tabernero E, Ruiz LA, España PP, Méndez R, Serrano L, Santos B, Uranga A, González P, Garcia P, Torres A, Menendez R, Zalacain R. COVID-19 in young and middle-aged adults: predictors of poor outcome and clinical differences. Infection 2021; 50:179-189. [PMID: 34463951 PMCID: PMC8406039 DOI: 10.1007/s15010-021-01684-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 08/10/2021] [Indexed: 12/27/2022]
Abstract
Introduction Young and middle-aged adults are the largest group of patients infected with SARS-CoV-2 and some of them develop severe disease. Objective To investigate clinical manifestations in adults aged 18–65 years hospitalized for COVID-19 and identify predictors of poor outcome. Secondary objectives: to explore differences compared to the disease in elderly patients and the suitability of the commonly used community-acquired pneumonia prognostic scales in younger populations. Methods Multicenter prospective registry of consecutive patients hospitalized for COVID-19 pneumonia aged 18–65 years between March and May 2020. We considered a composite outcome of “poor outcome” including intensive care unit admission and/or use of noninvasive ventilation, continuous positive airway pressure or high flow nasal cannula oxygen and/or death. Results We identified 513 patients < 65 years of age, from a cohort of 993 patients. 102 had poor outcomes (19.8%) and 3.9% died. 78% and 55% of patients with poor outcomes were classified as low risk based on CURB and PSI scores, respectively. A multivariate Cox regression model identified six independent factors associated with poor outcome: heart disease, absence of chest pain or anosmia, low oxygen saturation, high LDH and lymphocyte count < 800/mL. Conclusions COVID-19 in younger patients carries significant morbidity and differs in some respects from this disease in the elderly. Baseline heart disease is a relevant risk factor, while anosmia and pleuritic pain are associated to better prognosis. Hypoxemia, LDH and lymphocyte count are predictors of poor outcome. We consider that CURB and PSI scores are not suitable criteria for deciding admission in this population.
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Affiliation(s)
- Eva Tabernero
- Pneumology Service, Hospital Universitario Cruces, 48903, Barakaldo, Bizkaia, Spain. .,Biocruces Bizkaia Health Research Institute, Barakaldo, Spain.
| | - Luis A Ruiz
- Pneumology Service, Hospital Universitario Cruces, 48903, Barakaldo, Bizkaia, Spain.,Department of Immunology, Microbiology and Parasitology, Facultad de Medicina y Enfermería, Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU, Leioa, Bizkaia, Spain
| | - Pedro P España
- Pneumology Service, Hospital Universitario Galdakao-Usansolo, Galdakao, Bizkaia, Spain
| | - Raúl Méndez
- Pneumology Service, Hospital Universitari I Politècnic La Fe, Valencia, Spain.,Instituto de Investigación Sanitaria (IIS) La Fe, Valencia, Spain
| | - Leyre Serrano
- Pneumology Service, Hospital Universitario Cruces, 48903, Barakaldo, Bizkaia, Spain.,Department of Immunology, Microbiology and Parasitology, Facultad de Medicina y Enfermería, Universidad del País Vasco/Euskal Herriko Unibertsitatea UPV/EHU, Leioa, Bizkaia, Spain
| | - Borja Santos
- Bioinformatics and Statistics Unit, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | - Ane Uranga
- Pneumology Service, Hospital Universitario Galdakao-Usansolo, Galdakao, Bizkaia, Spain
| | - Paula González
- Pneumology Service, Hospital Universitari I Politècnic La Fe, Valencia, Spain.,Instituto de Investigación Sanitaria (IIS) La Fe, Valencia, Spain
| | - Patricia Garcia
- Pneumology Service, Hospital Universitario Galdakao-Usansolo, Galdakao, Bizkaia, Spain
| | - Antoni Torres
- Pneumology Service, Hospital Clinic/Institut D´Investigacions Biomediques August Pi I Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Rosario Menendez
- Pneumology Service, Hospital Universitari I Politècnic La Fe, Valencia, Spain.,Instituto de Investigación Sanitaria (IIS) La Fe, Valencia, Spain
| | - Rafael Zalacain
- Pneumology Service, Hospital Universitario Cruces, 48903, Barakaldo, Bizkaia, Spain
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35
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AlJame M, Imtiaz A, Ahmad I, Mohammed A. Deep forest model for diagnosing COVID-19 from routine blood tests. Sci Rep 2021; 11:16682. [PMID: 34404838 PMCID: PMC8371014 DOI: 10.1038/s41598-021-95957-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/03/2021] [Indexed: 02/07/2023] Open
Abstract
The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the limited quantity of swab tests, we propose a machine learning prediction model to accurately diagnose COVID-19 from clinical and/or routine laboratory data. The model exploits a new ensemble-based method called the deep forest (DF), where multiple classifiers in multiple layers are used to encourage diversity and improve performance. The cascade level employs the layer-by-layer processing and is constructed from three different classifiers: extra trees, XGBoost, and LightGBM. The prediction model was trained and evaluated on two publicly available datasets. Experimental results show that the proposed DF model has an accuracy of 99.5%, sensitivity of 95.28%, and specificity of 99.96%. These performance metrics are comparable to other well-established machine learning techniques, and hence DF model can serve as a fast screening tool for COVID-19 patients at places where testing is scarce.
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Affiliation(s)
- Maryam AlJame
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait.
| | | | - Imtiaz Ahmad
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait
| | - Ameer Mohammed
- Department of Computer Engineering, Kuwait University, Kuwait City, Kuwait
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Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R, Gorriz JM, Hussain S, Sani ZA, Moosaei H, Khosravi A, Nahavandi S, Islam SMS. Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep 2021; 11:15343. [PMID: 34321491 PMCID: PMC8319175 DOI: 10.1038/s41598-021-93543-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/25/2021] [Indexed: 02/07/2023] Open
Abstract
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
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Affiliation(s)
- Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Navid Hoseini Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | | | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia.
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam, 786004, India
| | | | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Bojnord, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, 3220, Australia
- Cardiovascular Division, The George Institute for Global Health, Newtown, Australia
- Sydney Medical School, University of Sydney, Camperdown, Australia
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Leite ML, de Loiola Costa LS, Cunha VA, Kreniski V, de Oliveira Braga Filho M, da Cunha NB, Costa FF. Artificial intelligence and the future of life sciences. Drug Discov Today 2021; 26:2515-2526. [PMID: 34245910 DOI: 10.1016/j.drudis.2021.07.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 05/12/2021] [Accepted: 07/01/2021] [Indexed: 12/23/2022]
Abstract
Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.
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Affiliation(s)
- Michel L Leite
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Department of Molecular Biology, Biological Sciences Institute, University of Brasília, Campus Darcy Ribeiro, Block K, 70.790-900, Brasilia, Federal District, Brazil
| | - Lorena S de Loiola Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor A Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Victor Kreniski
- Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil
| | | | - Nicolau B da Cunha
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil
| | - Fabricio F Costa
- Genomic Sciences and Biotechnology Program, Universidade Católica de Brasília SGAN 916 Modulo B, Bloco C, 70.790-160, Brasília, DF, Brazil; Apple Developer Academy, Universidade Católica de Brasília, Brasilia, Brazil; Cancer Biology and Epigenomics Program, Ann & Robert H Lurie Children's Hospital of Chicago Research Center and Northwestern University's Feinberg School of Medicine, 2430 N. Halsted St, Box 220, Chicago, IL 60614, USA; MATTER Chicago, 222 W. Merchandise Mart Plaza, Suite 12th Floor, Chicago, IL 60654, USA; Genomic Enterprise, San Diego, CA 92008, USA; Genomic Enterprise, New York, NY 11581, USA.
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38
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Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. COMPLEX INTELL SYST 2021; 7:2655-2678. [PMID: 34777970 PMCID: PMC8256231 DOI: 10.1007/s40747-021-00424-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/05/2021] [Indexed: 12/24/2022]
Abstract
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.
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Affiliation(s)
- Onur Dogan
- Department of Industrial Engineering, Izmir Bakircay University, 35665 Izmir, Turkey.,Research Center for Data Analytics and Spatial Data Modeling (RC-DAS), Izmir Bakircay University, 35665 Izmir, Turkey
| | - Sanju Tiwari
- Department of Computer Science, Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico
| | - M A Jabbar
- Vardhaman College of Engineering, Kacharam, India
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El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
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40
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Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126429. [PMID: 34198547 PMCID: PMC8296243 DOI: 10.3390/ijerph18126429] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/07/2021] [Accepted: 06/08/2021] [Indexed: 12/12/2022]
Abstract
The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.
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41
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Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021; 13:153-175. [PMID: 33886097 PMCID: PMC8060789 DOI: 10.1007/s12539-021-00431-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 04/03/2021] [Accepted: 04/09/2021] [Indexed: 12/23/2022]
Abstract
The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.
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Affiliation(s)
- Jawad Rasheed
- Department of Computer Engineering, Istanbul Aydin University, Istanbul, 34295, Turkey.
| | - Akhtar Jamil
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Alaa Ali Hameed
- Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
| | - Ahmad Rasheed
- Department of Electrical and Electronics Engineering, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey
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Shen B, Hoshmand-Kochi M, Abbasi A, Glass S, Jiang Z, Singer AJ, Thode HC, Li H, Hou W, Duong TQ. Initial chest radiograph scores inform COVID-19 status, intensive care unit admission and need for mechanical ventilation. Clin Radiol 2021; 76:473.e1-473.e7. [PMID: 33706997 PMCID: PMC7891126 DOI: 10.1016/j.crad.2021.02.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 02/08/2021] [Indexed: 12/15/2022]
Abstract
AIM To evaluate whether portable chest radiography (CXR) scores are associated with coronavirus disease 2019 (COVID-19) status and various clinical outcomes. MATERIALS AND METHODS This retrospective study included 500 initial CXR from COVID-19-suspected patients. Each CXR was scored based on geographic extent and degree of opacity as indicators of disease severity. COVID-19 status and clinical outcomes including intensive care unit (ICU) admission, mechanical ventilation, mortality, length of hospitalisation, and duration on ventilator were collected. Multivariable logistic regression analysis was performed to evaluate the relationship between CXR scores and COVID-19 status, CXR scores and clinical outcomes, adjusted for code status, age, gender and co-morbidities. RESULTS The interclass correlation coefficients amongst raters were 0.94 and 0.90 for the extent score and opacity score, respectively. CXR scores were significantly (p < 0.01) associated with COVID-19 positivity (odd ratio [OR] = 1.49; 95% confidence interval [CI]: 1.27 - 1.75 for extent score and OR = 1.75; 95% CI: 1.42 - 2.15 for opacity score), ICU admission (OR = 1.19; 95% CI: 1.09 - 1.31 for extent score and OR = 1.26; 95% CI: 1.10 - 1.44 for opacity score), and invasive mechanical ventilation (OR = 1.22; 95% CI: 1.11 - 1.35 for geographic score and OR = 1.21; 95% CI: 1.05 - 1.38 for opacity score). CXR scores were not significantly different between survivors and non-survivors after adjusting for code status (p>0.05). CXR scores were not associated with length of hospitalisation or duration on ventilation (p>0.05). CONCLUSIONS Initial CXR scores have prognostic value and are associated with COVID-19 positivity, ICU admission, and mechanical ventilation.
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Affiliation(s)
- B Shen
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - M Hoshmand-Kochi
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - A Abbasi
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - S Glass
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - Z Jiang
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - A J Singer
- Department of Emergency Medicine, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - H C Thode
- Department of Emergency Medicine, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - H Li
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - W Hou
- Department of Radiology, Renaissance School of Medicine at Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA
| | - T Q Duong
- Radiology, Montefiore Medical Center, 111 East 210(th) Street, Bronx, NY 10467, USA.
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43
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Lu JY, Babatsikos I, Fisher MC, Hou W, Duong TQ. Longitudinal Clinical Profiles of Hospital vs. Community-Acquired Acute Kidney Injury in COVID-19. Front Med (Lausanne) 2021; 8:647023. [PMID: 34124089 PMCID: PMC8193058 DOI: 10.3389/fmed.2021.647023] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 04/06/2021] [Indexed: 12/15/2022] Open
Abstract
Acute kidney injury (AKI) is associated with high mortality in coronavirus disease 2019 (COVID-19). However, it is unclear whether patients with COVID-19 with hospital-acquired AKI (HA-AKI) and community-acquired AKI (CA-AKI) differ in disease course and outcomes. This study investigated the clinical profiles of HA-AKI, CA-AKI, and no AKI in patients with COVID-19 at a large tertiary care hospital in the New York City area. The incidence of HA-AKI was 23.26%, and CA-AKI was 22.28%. Patients who developed HA-AKI were older and had more comorbidities compared to those with CA-AKI and those with no AKI (p < 0.05). A higher prevalence of coronary artery disease, heart failure, and chronic kidney disease was observed in those with HA-AKI compared to those with CA-AKI (p < 0.05). Patients with CA-AKI received more invasive and non-invasive mechanical ventilation, anticoagulants, and steroids compared to those with HA-AKI (p < 0.05), but patients with HA-AKI had significantly higher mortality compared to those with CA-AKI after adjusting for demographics and clinical comorbidities (adjusted odds ratio = 1.61, 95% confidence interval = 1.1-2.35, p < 0.014). In addition, those with HA-AKI had higher markers of inflammation and more liver injury (p < 0.05) compared to those with CA-AKI. These results suggest that HA-AKI is likely part of systemic multiorgan damage and that kidney injury contributes to worse outcomes. These findings provide insights that could lead to better management of COVID-19 patients in time-sensitive and potentially resource-constrained environments.
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Affiliation(s)
- Justin Y Lu
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY, United States
| | - Ioannis Babatsikos
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY, United States.,Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Molly C Fisher
- Division of Nephrology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY, United States
| | - Wei Hou
- Department of Family, Population & Preventive Medicine, Stony Brook Medicine, New York, NY, United States
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY, United States
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44
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Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021; 19:2833-2850. [PMID: 34025952 PMCID: PMC8123783 DOI: 10.1016/j.csbj.2021.05.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/01/2021] [Accepted: 05/02/2021] [Indexed: 12/23/2022] Open
Abstract
The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.
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Key Words
- ABG, Arterial Blood Gas
- ADA, Adenosine Deaminase
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APTT, Activated Partial Thromboplastin Time
- ARMED, Attribute Reduction with Multi-objective Decomposition Ensemble optimizer
- AUC, Area Under the Curve
- Acc, Accuracy
- Adaboost, Adaptive Boosting
- Apol AI, Apolipoprotein AI
- Apol B, Apolipoprotein B
- Artificial intelligence
- BNB, Bernoulli Naïve Bayes
- BUN, Blood Urea Nitrogen
- CI, Confidence Interval
- CK-MB, Creatine Kinase isoenzyme
- CNN, Convolutional Neural Networks
- COVID-19
- CPP, COVID-19 Positive Patients
- CRP, C-Reactive Protein
- CRT, Classification and Regression Decision Tree
- CoxPH, Cox Proportional Hazards
- DCNN, Deep Convolutional Neural Networks
- DL, Deep Learning
- DLC, Density Lipoprotein Cholesterol
- DNN, Deep Neural Networks
- DT, Decision Tree
- Diagnosis
- ED, Emergency Department
- ESR, Erythrocyte Sedimentation Rate
- ET, Extra Trees
- FCV, Fold Cross Validation
- FL, Federated Learning
- FiO2, Fraction of Inspiration O2
- GBDT, Gradient Boost Decision Tree
- GBM light, Gradient Boosting Machine light
- GDCNN, Genetic Deep Learning Convolutional Neural Network
- GFR, Glomerular Filtration Rate
- GFS, Gradient boosted feature selection
- GGT, Glutamyl Transpeptidase
- GNB, Gaussian Naïve Bayes
- HDLC, High Density Lipoprotein Cholesterol
- INR, International Normalized Ratio
- Inception Resnet, Inception Residual Neural Network
- L1LR, L1 Regularized Logistic Regression
- LASSO, Least Absolute Shrinkage and Selection Operator
- LDA, Linear Discriminant Analysis
- LDH, Lactate Dehydrogenase
- LDLC, Low Density Lipoprotein Cholesterol
- LR, Logistic Regression
- LSTM, Long-Short Term Memory
- MCHC, Mean Corpuscular Hemoglobin Concentration
- MCV, Mean corpuscular volume
- ML, Machine Learning
- MLP, MultiLayer Perceptron
- MPV, Mean Platelet Volume
- MRMR, Maximum Relevance Minimum Redundancy
- Multimodal data
- NB, Naïve Bayes
- NLP, Natural Language Processing
- NPV, Negative Predictive Values
- Nadam optimizer, Nesterov Accelerated Adaptive Moment optimizer
- OB, Occult Blood test
- PCT, Thrombocytocrit
- PPV, Positive Predictive Values
- PWD, Platelet Distribution Width
- PaO2, Arterial Oxygen Tension
- Paco2, Arterial Carbondioxide Tension
- Prognosis
- RBC, Red Blood Cell
- RBF, Radial Basis Function
- RBP, Retinol Binding Protein
- RDW, Red blood cell Distribution Width
- RF, Random Forest
- RFE, Recursive Feature Elimination
- RSV, Respiratory Syncytial Virus
- SEN, Sensitivity
- SG, Specific Gravity
- SMOTE, Synthetic Minority Oversampling Technique
- SPE, Specificity
- SRLSR, Sparse Rescaled Linear Square Regression
- SVM, Support Vector Machine
- SaO2, Arterial Oxygen saturation
- Screening
- TBA, Total Bile Acid
- TTS, Training Test Split
- WBC, White Blood Cell count
- XGB, eXtreme Gradient Boost
- k-NN, K-Nearest Neighbor
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Affiliation(s)
- Eleni S. Adamidi
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantinos Mitsis
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Konstantina S. Nikita
- Biomedical Simulations and Imaging Lab, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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Lan L, Sun W, Xu D, Yu M, Xiao F, Hu H, Xu H, Wang X. Artificial intelligence-based approaches for COVID-19 patient management. INTELLIGENT MEDICINE 2021; 1:10-15. [PMID: 34447600 PMCID: PMC8189732 DOI: 10.1016/j.imed.2021.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 03/27/2021] [Accepted: 05/21/2021] [Indexed: 01/08/2023]
Abstract
During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.
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46
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Chen A, Zhao Z, Hou W, Singer AJ, Li H, Duong TQ. Time-to-Death Longitudinal Characterization of Clinical Variables and Longitudinal Prediction of Mortality in COVID-19 Patients: A Two-Center Study. Front Med (Lausanne) 2021; 8:661940. [PMID: 33996864 PMCID: PMC8116568 DOI: 10.3389/fmed.2021.661940] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 04/06/2021] [Indexed: 12/15/2022] Open
Abstract
Objectives: To characterize the temporal characteristics of clinical variables with time lock to mortality and build a predictive model of mortality associated with COVID-19 using clinical variables. Design: Retrospective cohort study of the temporal characteristics of clinical variables with time lock to mortality. Setting: Stony Brook University Hospital (New York) and Tongji Hospital. Patients: Patients with confirmed positive for severe acute respiratory syndrome coronavirus-2 using polymerase chain reaction testing. Patients from the Stony Brook University Hospital data were used for training (80%, N = 1,002) and testing (20%, N = 250), and 375 patients from the Tongji Hospital (Wuhan, China) data were used for testing. Intervention: None. Measurements and Main Results: Longitudinal clinical variables were analyzed as a function of days from outcome with time-lock-to-day of death (non-survivors) or discharge (survivors). A predictive model using the significant earliest predictors was constructed. Performance was evaluated using receiver operating characteristics area under the curve (AUC). The predictive model found lactate dehydrogenase, lymphocytes, procalcitonin, D-dimer, C-reactive protein, respiratory rate, and white-blood cells to be early predictors of mortality. The AUC for the zero to 9 days prior to outcome were: 0.99, 0.96, 0.94, 0.90, 0.82, 0.75, 0.73, 0.77, 0.79, and 0.73, respectively (Stony Brook Hospital), and 1.0, 0.86, 0.88, 0.96, 0.91, 0.62, 0.67, 0.50, 0.63, and 0.57, respectively (Tongji Hospital). In comparison, prediction performance using hospital admission data was poor (AUC = 0.59). Temporal fluctuations of most clinical variables, indicative of physiological and biochemical instability, were markedly higher in non-survivors compared to survivors (p < 0.001). Conclusion: This study identified several clinical markers that demonstrated a temporal progression associated with mortality. These variables accurately predicted death within a few days prior to outcome, which provides objective indication that closer monitoring and interventions may be needed to prevent deterioration.
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Affiliation(s)
- Anne Chen
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States.,Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Zirun Zhao
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States.,Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Wei Hou
- Department of Family Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Adam J Singer
- Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Haifang Li
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States.,Department of Radiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Tim Q Duong
- Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY, United States
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47
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Lu JQ, Musheyev B, Peng Q, Duong TQ. Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: a retrospective study. PeerJ 2021; 9:e11205. [PMID: 33976972 PMCID: PMC8061580 DOI: 10.7717/peerj.11205] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 03/11/2021] [Indexed: 12/14/2022] Open
Abstract
This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients hospitalized in the general floor will need escalated care early on using neural networks (NNs). Analysis was performed on hospitalized COVID-19 patients between 7 February 2020 and 4 May 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs and blood gases were collected. We compared those data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: (i) COVID-19 patients admitted to the general floor (N = 1203) vs. those directly admitted to ICU (N = 104), and (ii) patients not upgraded to ICU (N = 979) vs. those upgraded to the ICU (N = 224) from the general floor. A NN algorithm was used to predict ICU admission, with 80% training and 20% testing. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis (ROC). We found that C-reactive protein, lactate dehydrogenase, creatinine, white-blood cell count, D-dimer and lymphocyte count showed temporal divergence between COVID-19 patients hospitalized in the general floor that were upgraded to ICU compared to those that were not. The NN predictive model essentially ranked the same laboratory variables to be important predictors of needing ICU care. The AUC for predicting ICU admission was 0.782 ± 0.013 for the test dataset. Adding vital sign and blood-gas data improved AUC (0.822 ± 0.018). This work could help frontline physicians to anticipate downstream ICU need to more effectively allocate healthcare resources.
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Affiliation(s)
- Joyce Q Lu
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Benjamin Musheyev
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Qi Peng
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
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48
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Musheyev B, Borg L, Janowicz R, Matarlo M, Boyle H, Singh G, Ende V, Babatsikos I, Hou W, Duong TQ. Functional status of mechanically ventilated COVID-19 survivors at ICU and hospital discharge. J Intensive Care 2021; 9:31. [PMID: 33789772 PMCID: PMC8011060 DOI: 10.1186/s40560-021-00542-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 03/02/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND A significant number of COVID-19 patients have been treated using invasive mechanical ventilation (IMV). The ability to evaluate functional status of COVID-19 survivors early on at ICU and hospital discharge may enable identification of patients who may need medical and rehabilitation interventions. METHODS The modified "Mental Status", ICU Mobility, and Barthel Index scores at ICU and hospital discharge were tabulated for 118 COVID-19 survivors treated with invasive mechanical ventilation (IMV). These functional scores were compared with pre-admission functional status, discharge durable medical equipment, discharge medical follow-up recommendation, duration on IMV, duration post-IMV, demographics, comorbidities, laboratory tests, and vital signs at ICU and hospital discharge. RESULTS The majority of COVID-19 IMV patients were not functionally independent at hospital discharge (22% discharged with cane or rolling walker, 49% discharged with durable medical equipment, and 14% admitted to a rehabilitation facility), although 94% of these patients were functionally independent prior to COVID-19 illness. Half of the patients were discharged with supplemental oxygen equipment. The most prevalent medical follow-up recommendations were cardiology, vascular medicine, pulmonology, endocrinology, and neurology with many patients receiving multiple medical follow-up recommendations. Functional status improved from ICU discharge to hospital discharge (p < 0.001). Worse functional status at hospital discharge was associated with longer IMV duration, older age, male sex, higher number of comorbidities, and the presence of pre-existing comorbidities including hypertension, diabetes, chronic obstructive pulmonary disease, and immunosuppression (p < 0.05, ANOVA). CONCLUSIONS The majority of IMV COVID-19 survivors were not functionally independent at discharge and required significant follow-up medical care. The COVID-19 circumstance has placed constraints on access to in-hospital rehabilitation. These findings underscore the need for prospective studies to ascertain the short- and long-term sequela in COVID-19 survivors.
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Affiliation(s)
- Benjamin Musheyev
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Lara Borg
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Rebeca Janowicz
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Michael Matarlo
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Hayle Boyle
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Gurinder Singh
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Victoria Ende
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Ioannis Babatsikos
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Wei Hou
- Department of Family, Population and Preventative Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA.
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49
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Tayarani N MH. Applications of artificial intelligence in battling against covid-19: A literature review. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110338. [PMID: 33041533 PMCID: PMC7532790 DOI: 10.1016/j.chaos.2020.110338] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/01/2020] [Indexed: 05/14/2023]
Abstract
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
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Affiliation(s)
- Mohammad-H Tayarani N
- Biocomputation Group, School of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom
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50
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Zhu JS, Ge P, Jiang C, Zhang Y, Li X, Zhao Z, Zhang L, Duong TQ. Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients. J Am Coll Emerg Physicians Open 2020; 1:1364-1373. [PMID: 32838390 PMCID: PMC7405082 DOI: 10.1002/emp2.12205] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/01/2020] [Accepted: 07/13/2020] [Indexed: 01/01/2023] Open
Abstract
Objective The large number of clinical variables associated with coronavirus disease 2019 (COVID-19) infection makes it challenging for frontline physicians to effectively triage COVID-19 patients during the pandemic. This study aimed to develop an efficient deep-learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID-19 patients. Methods This retrospective study consisted of 181 hospitalized patients with confirmed COVID-19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep-learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance was compared with those using COVID-19 severity score, CURB-65 score, and pneumonia severity index (PSI). Results Of the 181 COVID-19 patients, 39 expired and 142 survived. Five top predictors of mortality were D-dimer, O2 Index, neutrophil:lymphocyte ratio, C-reactive protein, and lactate dehydrogenase. The top 5 predictors and the resultant risk score yielded, respectively, an area under curve (AUC) of 0.968 (95% CI = 0.87-1.0) and 0.954 (95% CI = 0.80-0.99) for the testing dataset. Our models outperformed COVID-19 severity score (AUC = 0.756), CURB-65 score (AUC = 0.671), and PSI (AUC = 0.838). The mortality rates for our risk stratification scores (0-5) were 0%, 0%, 6.7%, 18.2%, 67.7%, and 83.3%, respectively. Conclusions Deep-learning prediction model and the resultant risk stratification score may prove useful in clinical decisionmaking under time-sensitive and resource-constrained environment.
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Affiliation(s)
- Jocelyn S Zhu
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
| | - Peilin Ge
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
| | - Chunguo Jiang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang HospitalCapital Medical UniversityBeijingChina
| | - Yong Zhang
- Department of Hepatobiliary Surgery, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
| | - Xiaoran Li
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
| | - Zirun Zhao
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
| | - Liming Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang HospitalCapital Medical UniversityBeijingChina
| | - Tim Q. Duong
- Departments of Radiology, Renaissance School of MedicineStony Brook UniversityStony BrookNew YorkUSA
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