Search

Your search keyword '"Dwivedi, Girish"' showing total 1,018 results
Start Over Author "Dwivedi, Girish"
1,018 results on '"Dwivedi, Girish"'

1. Effect of Testosterone Treatment on Cardiovascular Events in Men: Protocol for a Systematic Literature Review and Meta-Analysis

Author

Chih, HuiJun, Reid, Christopher M, Yeap, Bu B, and Dwivedi, Girish

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundTestosterone prescriptions have increased dramatically in recent decades, with increasing usage in men. Despite epidemiological associations reported high circulating concentrations of endogenous androgens and low risk of cardiovascular events and mortality, the effects of exogenous androgens in the form of testosterone therapy for maintaining physiological circulating androgen concentrations on the cardiovascular system remain uncertain with no published meta-analysis on this topic. ObjectiveThe aim of this study was to investigate the effects of prescribed testosterone treatment, in all forms and durations, from well-developed randomized controlled trials, on cardiovascular events in men aged 18 years or older. MethodsPeer-reviewed journal articles published from 1980 to 2019 will be searched from databases (ie CINAHL [Cumulated Index to Nursing and Allied Health Literature], Embase, Medline, Scopus, Cochrane Controlled Register of Trials as well as the Clinical Trial Registry). Randomized controlled trials or cluster randomized controlled trials with at least one intervention arm of testosterone and a control group of usual care or no testosterone treatment will be included in this review and meta-analysis. Studies on men with previous cardiovascular events or cardiac vascularization (coronary bypass surgery or percutaneous coronary intervention) will be excluded. Data related to primary outcomes such as clinical events of any type of stroke or transient ischemic attack, nonfatal myocardial infarction or acute coronary syndrome, emergency coronary artery revascularization, carotid surgery, cardiac mortality, and all-cause mortality will be extracted for analysis. The criteria for PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) will be followed in the evaluation of evidence. ResultsSearch terms have been piloted and finalized. This study will be completed by the end of 2020. ConclusionsThis protocol will guide a systematic literature review of the evidence around prescribed testosterone and its effect on cardiovascular events in men aged 18 years or older. The findings will inform clinical management of hypogonadal men. Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42019134278; https://tinyurl.com/y6t7ggge International Registered Report Identifier (IRRID)PRR1-10.2196/15163

Published
2020
Full Text
View/download PDF

2. Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients

Author

Lu, Juan, Bennamoun, Mohammed, Stewart, Jonathon, Eshraghian, JasonK., Liu, Yanbin, Chow, Benjamin, Sanfilippo, Frank M., and Dwivedi, Girish

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

Diagnostic investigation has an important role in risk stratification and clinical decision making of patients with suspected and documented Coronary Artery Disease (CAD). However, the majority of existing tools are primarily focused on the selection of gatekeeper tests, whereas only a handful of systems contain information regarding the downstream testing or treatment. We propose a multi-task deep learning model to support risk stratification and down-stream test selection for patients undergoing Coronary Computed Tomography Angiography (CCTA). The analysis included 14,021 patients who underwent CCTA between 2006 and 2017. Our novel multitask deep learning framework extends the state-of-the art Perceiver model to deal with real-world CCTA report data. Our model achieved an Area Under the receiver operating characteristic Curve (AUC) of 0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests. Our proposed deep learning model can accurately estimate the likelihood of CAD and provide recommended downstream tests based on prior CCTA data. In clinical practice, the utilization of such an approach could bring a paradigm shift in risk stratification and downstream management. Despite significant progress using deep learning models for tabular data, they do not outperform gradient boosting decision trees, and further research is required in this area. However, neural networks appear to benefit more readily from multi-task learning than tree-based models. This could offset the shortcomings of using single task learning approach when working with tabular data.

Published
2023

3. Capturing the pulse: a state-of-the-art review on camera-based jugular vein assessment.

Author

Arrow, Coen, Ward, Max, Eshraghian, Jason, and Dwivedi, Girish

Abstract

Heart failure is associated with a rehospitalisation rate of up to 50% within six months. Elevated central venous pressure may serve as an early warning sign. While invasive procedures are used to measure central venous pressure for guiding treatment in hospital, this becomes impractical upon discharge. A non-invasive estimation technique exists, where the clinician visually inspects the pulsation of the jugular veins in the neck, but it is less reliable due to human limitations. Video and signal processing technologies may offer a high-fidelity alternative. This state-of-the-art review analyses existing literature on camera-based methods for jugular vein assessment. We summarize key design considerations and suggest avenues for future research. Our review highlights the neck as a rich imaging target beyond the jugular veins, capturing comprehensive cardiac signals, and outlines factors affecting signal quality and measurement accuracy. Addressing an often quoted limitation in the field, we also propose minimum reporting standards for future studies.

Published
2023

4. FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Author

Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, and Starmans, Martijn P A

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.0, I.4.0, I.5.0
Abstract

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

Published
2023

6. Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment

Author

Tang, Hao, Rekavandi, Aref Miri, Rooprai, Dharjinder, Dwivedi, Girish, Sanfilippo, Frank, Boussaid, Farid, and Bennamoun, Mohammed

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Quantitative Biology - Neurons and Cognition
Abstract

This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks and identifying the dominant causes for such behaviours. Data augmentation techniques and ML models are utilized to predict the associated risk. Furthermore, SHapley Additive exPlanations (SHAP) and correlation analysis are used to rank the importance of variables in predictions. Experimental results indicate that Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models achieve the best results while DT has the best performance with an accuracy of 95:23% and an Area Under Curve (AUC) of 0.95. As per SHAP results, anger problems, depression, and social isolation are the leading variables in predicting the risk of suicide, and patients with good incomes, respected occupations, and university education have the least risk. Results demonstrate the effectiveness of machine learning and XAI framework for suicide risk prediction, and they can assist psychiatrists in understanding complex human behaviours and can also assist in reliable clinical decision-making.

Published
2023

7. 3D Brain and Heart Volume Generative Models: A Survey

Author

Liu, Yanbin, Dwivedi, Girish, Boussaid, Farid, and Bennamoun, Mohammed

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, 92C55 (Primary), 68U10 (Secondary), I.4, J.3
Abstract

Generative models such as generative adversarial networks and autoencoders have gained a great deal of attention in the medical field due to their excellent data generation capability. This paper provides a comprehensive survey of generative models for three-dimensional (3D) volumes, focusing on the brain and heart. A new and elaborate taxonomy of unconditional and conditional generative models is proposed to cover diverse medical tasks for the brain and heart: unconditional synthesis, classification, conditional synthesis, segmentation, denoising, detection, and registration. We provide relevant background, examine each task and also suggest potential future directions. A list of the latest publications will be updated on Github to keep up with the rapid influx of papers at https://github.com/csyanbin/3D-Medical-Generative-Survey., Comment: Accepted at ACM Computing Surveys (CSUR) 2023

Published
2022
Full Text
View/download PDF

8. Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images

Author

Liu, Yanbin, Dwivedi, Girish, Boussaid, Farid, Sanfilippo, Frank, Yamada, Makoto, and Bennamoun, Mohammed

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, I.4, J.3
Abstract

The generation of three-dimensional (3D) medical images has great application potential since it takes into account the 3D anatomical structure. Two problems prevent effective training of a 3D medical generative model: (1) 3D medical images are expensive to acquire and annotate, resulting in an insufficient number of training images, and (2) a large number of parameters are involved in 3D convolution. Methods: We propose a novel GAN model called 3D Split&Shuffle-GAN. To address the 3D data scarcity issue, we first pre-train a two-dimensional (2D) GAN model using abundant image slices and inflate the 2D convolution weights to improve the initialization of the 3D GAN. Novel 3D network architectures are proposed for both the generator and discriminator of the GAN model to significantly reduce the number of parameters while maintaining the quality of image generation. Several weight inflation strategies and parameter-efficient 3D architectures are investigated. Results: Experiments on both heart (Stanford AIMI Coronary Calcium) and brain (Alzheimer's Disease Neuroimaging Initiative) datasets show that our method leads to improved 3D image generation quality (14.7 improvements on Fr\'echet inception distance) with significantly fewer parameters (only 48.5% of the baseline method). Conclusions: We built a parameter-efficient 3D medical image generation model. Due to the efficiency and effectiveness, it has the potential to generate high-quality 3D brain and heart images for real use cases., Comment: Published at Computer Methods and Programs in Biomedicine (CMPB) 2023

Published
2022
Full Text
View/download PDF

14. Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation

Author

Lu, Juan, Hutchens, Rebecca, Hung, Joseph, Bennamoun, Mohammed, McQuillan, Brendan, Briffa, Tom, Sohel, Ferdous, Murray, Kevin, Stewart, Jonathon, Chow, Benjamin, Sanfilippo, Frank, and Dwivedi, Girish

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

Appropriate antithrombotic therapy for patients with atrial fibrillation (AF) requires assessment of ischemic stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Machine learning (ML) techniques may improve predictive performance and support decision-making for appropriate antithrombotic therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients. Materials and Methods This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The primary outcome was ischemic stroke and major bleeding admission. The secondary outcomes were all-cause death and event-free survival. The discriminant power of ML models was compared with clinical risk scores by the area under the curve (AUC). Risk stratification was assessed using the net reclassification index. Results Multilabel gradient boosting machine provided the best discriminant power for stroke, major bleeding, and death (AUC = 0.685, 0.709, and 0.765 respectively) compared to other ML models. It provided modest performance improvement for stroke compared to CHA2DS2-VASc (AUC = 0.652), but significantly improved major bleeding prediction compared to HAS-BLED (AUC = 0.522). It also had a much greater discriminant power for death compared with CHA2DS2-VASc (AUC = 0.606). Also, models identified additional risk features (such as hemoglobin level, renal function, etc.) for each outcome. Conclusions Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.

Published
2022

15. Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?

Author

Ward, Isaac Ronald, Wang, Ling, lu, Juan, Bennamoun, Mohammed, Dwivedi, Girish, and Sanfilippo, Frank M

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that take an individual's health information (e.g. their drug history and comorbidities) as inputs, and predict the probability that the individual will have an Acute Coronary Syndrome (ACS) adverse outcome. Using XAI, we quantified the contribution that specific drugs had on these ACS predictions, thus creating an XAI-based technique for pharmacovigilance monitoring, using ACS as an example of the adverse outcome to detect. Individuals aged over 65 who were supplied Musculo-skeletal system (anatomical therapeutic chemical (ATC) class M) or Cardiovascular system (ATC class C) drugs between 1993 and 2009 were identified, and their drug histories, comorbidities, and other key features were extracted from linked Western Australian datasets. Multiple ML models were trained to predict if these individuals would have an ACS related adverse outcome (i.e., death or hospitalisation with a discharge diagnosis of ACS), and a variety of ML and XAI techniques were used to calculate which features -- specifically which drugs -- led to these predictions. The drug dispensing features for rofecoxib and celecoxib were found to have a greater than zero contribution to ACS related adverse outcome predictions (on average), and it was found that ACS related adverse outcomes can be predicted with 72% accuracy. Furthermore, the XAI libraries LIME and SHAP were found to successfully identify both important and unimportant features, with SHAP slightly outperforming LIME. ML models trained on linked administrative health datasets in tandem with XAI algorithms can successfully quantify feature importance, and with further development, could potentially be used as pharmacovigilance monitoring techniques., Comment: Comput Methods Programs Biomed. 2021 Nov;212:106415. Epub 2021 Sep 26

Published
2021
Full Text
View/download PDF

16. Training Spiking Neural Networks Using Lessons From Deep Learning

Author

Eshraghian, Jason K., Ward, Max, Neftci, Emre, Wang, Xinxin, Lenz, Gregor, Dwivedi, Girish, Bennamoun, Mohammed, Jeong, Doo Seok, and Lu, Wei D.

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Emerging Technologies, Computer Science - Machine Learning
Abstract

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks. We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to spiking neural networks (SNNs); the subtle link between temporal backpropagation and spike timing dependent plasticity, and how deep learning might move towards biologically plausible online learning. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here. The fields of deep learning and spiking neural networks evolve very rapidly. We endeavour to treat this document as a 'dynamic' manuscript that will continue to be updated as the common practices in training SNNs also change. A series of companion interactive tutorials complementary to this paper using our Python package, snnTorch, are also made available. See https://snntorch.readthedocs.io/en/latest/tutorials/index.html .

Published
2021

18. Absence of Pericarditis Recurrence in Rilonacept-Treated Patients With COVID-19 and SARS-CoV-2 Vaccination: Results From the RHAPSODY Long-term Extension

Author

Abbate, Antonio, Abo-Auda, Wael, Akhtar, Asif, Arad, Michael, Atar, Shaul, Baibhav, Bipul, Brucato, Antonio, Collins, Sean, Colquhoun, David, Cremer, Paul, Cross, David, Dwivedi, Girish, Eisen, Alon, Freedberg, Nahum, Fuchs, Shmuel, Gaddam, Eliyazar, Gattorno, Marco, Gelfand, Eli, Grena, Paul, Halabi, Majdi, Harris, David, Imazio, Massimo, Insalaco, Antonella, Karim, Amin, Klein, Allan, Knowlton, Kirk, Kontzias, Apostolos, Kornberg, Robert, Latif, Faisal, Leibowitz, David, LeWinter, Martin, Lou, Pey Wen, Lin, David, Luis, S. Allen, Nicholls, Stephen, Petersen, John, Portman, Michael, Roberts-Thomson, Philip, Schiff, Elad, Siegel, Robert, Stokes, Michael, Sutej, Paul, Wittekind, Samuel, Witzling, Valentin, Zukermann, Robert, Trotta, Lucia, Cremer, Paul C., Klutstein, Marc, Luis, Sushil A., Wasserstrum, Yishay, Clair, JoAnn, Wang, Sheldon, Klein, Allan L., and Paolini, John F.

Published
2024
Full Text
View/download PDF

23. Imputation of Missing Data with Class Imbalance using Conditional Generative Adversarial Networks

Author

Awan, Saqib Ejaz, Bennamoun, Mohammed, Sohel, Ferdous, Sanfilippo, Frank M, and Dwivedi, Girish

Subjects
Computer Science - Machine Learning
Abstract

Missing data is a common problem faced with real-world datasets. Imputation is a widely used technique to estimate the missing data. State-of-the-art imputation approaches, such as Generative Adversarial Imputation Nets (GAIN), model the distribution of observed data to approximate the missing values. Such an approach usually models a single distribution for the entire dataset, which overlooks the class-specific characteristics of the data. Class-specific characteristics are especially useful when there is a class imbalance. We propose a new method for imputing missing data based on its class-specific characteristics by adapting the popular Conditional Generative Adversarial Networks (CGAN). Our Conditional Generative Adversarial Imputation Network (CGAIN) imputes the missing data using class-specific distributions, which can produce the best estimates for the missing values. We tested our approach on benchmark datasets and achieved superior performance compared with the state-of-the-art and popular imputation approaches.

Published
2020

35. FRI-263 Severity of hepatic steatosis measured with controlled attenuation parameter as a determinant of carotid atherosclerosis in the Raine Study

Author

Ayonrinde, Oyekoya, primary, Adams, Leon, additional, Zelesco, Marilyn, additional, Welman, Christopher, additional, Mori, Trevor, additional, Melton, Phillip, additional, Mould, Andrea, additional, Beilin, Lawrence, additional, Fiori, James, additional, Dwivedi, Girish, additional, Sanfilippo, Frank, additional, and Olynyk, John, additional

Published
2024
Full Text
View/download PDF

36. FRI-250 An intergenerational study of the prevalence and cardiometabolic significance of steatotic liver disease in adults in the Raine Study

Author

Ayonrinde, Oyekoya, primary, Adams, Leon, additional, Mori, Trevor, additional, Melton, Phillip, additional, Olynyk, John, additional, Zelesco, Marilyn, additional, Mould, Andrea, additional, Fiori, James, additional, Beilin, Lawrence, additional, Dwivedi, Girish, additional, Sanfilippo, Frank, additional, and Welman, Christopher, additional

Published
2024
Full Text
View/download PDF

44. Worldwide Disparities in Recovery of Cardiac Testing 1 Year Into COVID-19

Author

Einstein, Andrew J., Paez, Diana, Dondi, Maurizio, Better, Nathan, Cerci, Rodrigo, Dorbala, Sharmila, Karthikeyan, Ganesan, Pascual, Thomas N.B., Shaw, Leslee J., Villines, Todd C., Vitola, Joao V., Williams, Michelle C., Pynda, Yaroslav, Hinterleitner, Gerd, Lu, Yao, Morozova, Olga, Xu, Zhuoran, Hirschfeld, Cole B., Cohen, Yosef, Erinne, Ikenna, Malkovskiy, Eli, Randazzo, Michael, Sewanan, Lorenzo, Shetty, Mrinali, Choi, Andrew, Lopez-Mattei, Juan, Parwani, Purvi, Goda, Artan, Shirka, Ervina, Bouyoucef, Salah, Chelghoum, Lydia, Mansouri, Farouk, Medjahedi, Abdelkader, Naili, Qais, Ridouh, Mokhtar, Alasia, Diego, Alberghina, Lucia, Aramayo, Natalia, Buchara, Diego, Busso, Franco Gabriel, Bustos Rivadero, Jose Javier, Camilletti, Jorge, Campanelli, Hugo, Campisi, Roxana, Castro, Ricardo Belisario, Daicz, Mariana, del Riego, Horacio, Dragonetti, Laura, Echazarreta, Diego, Erriest, Juan, Faccio, Fernando, Facello, Adolfo, Gallegos, Hugo, Geronazzo, Ricardo, Glait, Horacio, Hasbani, Victor, Jäger, Victor, Lewkowicz, Julio Manuel, Lotti, Jose, Maciel, Neiva, Masoli, Osvaldo, Mastrovito, Edgardo, Medus, Maria, Merani, Maria Fernanda, Molteni, Susana, Montecinos, Marcos, Parisi, Gustavo, Sueldo, Claudio Pereyra, Perez de Arenaza, Diego, Quintana, Luis, Radzinschi, Alejandro, Redruello, Marcela, Rodríguez, Marina, Rojas, Horacio, Acuña, Arturo Romero, Schere, Daniel, Traverso, Sonia, Vazquez, Gustavo, Zeffiro, Susana, Sakanyan, Mari, Beuzeville, Scott, Boktor, Raef, Crowley, Michael, Downie, D'Arne, Dwivedi, Girish, Elison, Barry, Farouque, Omar, Jasper, Kim, Joshi, Subodh, Lee, Joseph, Lee, Kenneth, Lui, Elaine, Mcconachie, Peter, Meaker, Joanne, Nandurkar, Dee, Neill, Johanne, O'Rourke, Edward, O'Sullivan, Patricia, Pandos, George, Premaratne, Manuja, Prior, David, Rutherford, Natalie, Saunders, Connor, Taubman, Kim, Tauro, Andrew, Taylor, Andrew, Theuerle, James, Thomas, Paul, Tow, Jonathan, Upton, Anthony, Vamadevan, Shankar, Wayne, Victor, Wegner, Eva Alina, Wong, David, Younger, John, Beitzke, Dietrich, Feuchtner, Gudrun, Sommer, Oliver, Weiss, Konrad, Maroz-Vadalazhskaya, Natallia, Tserakhau, Uladzimir, Homans, Filip, Van De Heyning, Caroline M., Araujo, Raúl, Soldat-Stankovic, Valentina, Stankovic, Sinisa, Almeida, Augusto, Anselmi, Carlos, Azevedo, Guilherme S.A., Bittencourt, Marcio Sommer, Pianta, Diego Bromfman, Cabeda, Estevan, Carreira, Lara, Coelho, Igor, de Amorim Fernandes, Fernando, de Lorenzo, Andrea, Delgado, Roberta, Erthal, Fernanda, Fernandes, Fabio, Fernandes, Juliano, Ferreira de Souza, Thiago, Foppa, Murilo, Matos Alves, Wilson Furlan, Gontijo, Cibele, Gottlieb, Ilan, Grossman, Gabriel, Albernaz Siqueira, Maria Helena, Nomura, Cesar Higa, Koga, Katia Hiromoto, Lima, Ronaldo, Lopes, Rafael, Marçal Filho, Hugo Humberto, Masiero, Paulo, Mastrocola, Luiz, Menezes de Siqueira, Maria Eduarda, Mesquita, Claudio, Naves, Danilo, Penna, Filipe, Pinto, Ibraim, Rocha, Thércio, Rocha, Juliana Leal, Rodrigues, Alfredo, Salioni, Leila, Sanches, Adelina, Santos, Marcelo, Da Silva, Leonardo Sara, Schvartzman, Paulo, Matushita, Cristina Sebastião, Senra, Tiago, Silva, Marcelo, Soares, Carlos Eduardo, Spiro, Bernardo, Suaide Silva, Carlos Eduardo, Torres, Rafael, Monte, Guilherme Urpia, Vilela, Andrea, Villa, Alexandre Volney, Vitola, Joao, Voss, Themissa, Waltrick, Roberto, Zapparoli, Marcello, Naseer, Hamid, Garcheva-Tsacheva, Marina, Ouattara, Tiémégna Florence, Thou, Sarameth, Varoeun, Soley, Abikhzer, Gad, Beanlands, Rob, Chetrit, Michael, Dabreo, Dominique, Dennie, Carole, Friedrich, Matthias, Hafez, Mohmmed Nassoh, Hanneman, Kate, Miller, Robert, Oikonomou, Anastasia, Roifman, Idan, Small, Gary, Tandon, Vikas, Trivedi, Adwait, White, James, Zukotynski, Katherine, Alay, Rita, Concha, Carmen, Massardo, Teresa, Abad, Pedro, Anzola, Kelly, Arturo, Harold, Benitez, Luis, Cadena, Alberto, Zamudio, Carlos Caicedo, Calderón, Antonio, Gutierrez Villamil, Claudia T., Jaimes, Claudia, Londono, Juan L., Lopez, Nelson, Merlano-Gaitan, Sonia, Murgieitio-Cabrera, Ramon, Valencia, Manuel, Vergel, Damiana, Santamaria, Alejandro Zuluaga, Solis, Felix, Batinic, Tonci, Franceschi, Maja, Paar, Maja Hrabak, Prpic, Marina, Felipe Batista, Cuba: Juan, Cabrera, Lazaro Omar, Peix, Amalia, Peña, Yamilé, Rochela Vázquez, Luis Manuel, Ntalas, Ioannis, Kaminek, Milan, Kincl, Vladimir, Lang, Otto, Abdulla, Jawdat, Bøttcher, Morten, Busk, Martin, Geisler, Uka, Gormsen, Lars C., Hansson, Nicolaj, Hess, Søren, Hove, Jens, Jensen, Lars Thorbjoern, Jensen, Magnus T., Kragholm, Kristian Hay, Nørgaard, Bjarne L., Øvrehus, Kristian, Rasmussen, Jan, Rønnow Sand, Niels Peter, Sondergaard, Hanne, Zaremba, Tomas, Speckter, Herwin, Amores, Nelson, Velez, Mayra Sanchez, Alrahman, Taghreed Abd, Elsamad, Sherif Abd, Abdelfattah, Alia, Allam, Adel, Elkaffas, Sameh, Hassan, Mona, Hussein, Elshaymaa, Ibrahim, Ahmed, Kandeel, Ahmed, Ali, Mohamed Mandour, Shaaban, Mahmoud, Flores, Camila, Gómez Leiva, Verónica Vanesa, Liiver, Anita, Larikka, Martti, Uusitalo, Valtteri, Agostini, Denis, Berger, Clothilde, Dietz, Matthieu, Hyafil, Fabien, Ohana, Mickaël, Prigent, Kevin, Regaieg, Hamza, Sarda-Mantel, Laure, H-Ici, Darach O., Ayetey, Harold, Angelidis, George, Fragkaki, Christina, Fragkiadaki, Chrysoula, Georgoulias, Panagiotis, Koutelou, Maria, Kyrozi, Elena, Lama, Niki, Prassopoulos, Vassilis, Spartalis, Michael, Zaglavara, Theodora, Gonzalez, Carla, Gutierrez, Goleat, Maldonado, Alejandro, Martinez, Yassine, Kovács, Attila, Szilveszter, Bálint, Banthia, Nilesh, Bhat, Vivek, Bhatia, Mona, Choudhury, Partha, Chowdekar, Vijay Sai, Christopher, Johann, Garg, Tushar, Goyal, Naresh Kumar, Gupta, Ripen Kumar, Gupta, Abhishek, Hephzibah, Julie, Jain, Shashank, Krupa, Jesu, Kumar, Parveen, Kumar, Sukriti, Lalchandani, Arati, Mishra, Animesh, Mishra, Vivaswan Dutt, Mohan, Parul, Ozair, Ahmad, Pandey, Shivani, Parameswaran, Ramanathapuram, Patel, Chetan, Patel, Tapan, Patel, Shivani, Vimala, Leena Robinson, Kumar Sarangi, Dr Pradosh, Sengupta, Shantanu, Sethi, Arvind, Sharma, Amit, Sharma, Awadhesh Kumar, Sharma, Punit, Shrigiriwar, Apurva, Singh, Santosh, Singh, Harpreet, Sood, Ashwani, Verma, Atul, Vyas, Ajay, Soeriadi, Erwin Affandi, Bun, Edison, Hutomo, Febby, Syawaluddin, Hilman, Yudistiro, Ryan, Albadr, Amjed, Assadi, Majid, Emami, Farshad, Emami-Ardekani, Alireza, Farzanehfar, Saeed, Jafari, Ramezan, Manafi-Farid, Reyhaneh, Tajik, Maryam, Arnson, Yoav, Fuchs, Shmuel, Goldkorn, Ronen, Kennedy, John, Leitman, Marina, Shalev, Aryeh, Acampa, Wanda, Albano, Domenico, Alongi, Pierpaolo, Arnone, Gaspare, Assante, Roberta, Baritussio, Anna, Bauckneht, Matteo, Bianco, Francesco, Bonfiglioli, Rachele, Bovenzi, Francesco, Bruno, Isabella, Bruno, Andrea, Busnardo, Elena, Califaretti, Elena, Casoni, Roberta, Censullo, Vittorio, Chierichetti, Franca, Chiocchi, Marcello, Cittanti, Corrado, Clemente, Alberto, Cuocolo, Alberto, De Rimini, Maria Luisa, De Vincentis, Giuseppe, Della Tommasina, Veronica, Dellegrottaglie, Santo, Erba, Paola Anna, Evangelista, Laura, Faggi, Lara, Faragasso, Evelina, Florimonte, Luigia, Frantellizzi, Viviana, Gatti, Marco, Gaudiano, Angela, Gelardi, Fabrizia, Gerali, Alberto, Gimelli, Alessia, Guglielmo, Marco, Leccisotti, Lucia, Liga, Riccardo, Liguori, Carlo, Longo, Giampiero, Maffione, Margherita, Marcassa, Claudio, Matassa, Giovanni, Mele, Donato, Milan, Elisa, Mircoli, Luca, Paccagnella, Andrea, Pacella, Sara, Padovano, Federica, Pellegrini, Dario, Pergola, Valeria, Pugliese, Luca, Quartuccio, Natale, Rampin, Lucia, Ricci, Fabrizio, Rubini, Giuseppe, Russo, Vincenzo, Sambuceti, Gianmario, Scatteia, Alessandra, Sciagrà, Roberto, Spidalieri, Gianluca, Stefanelli, Antonella, Tedeschi, Carlo, Ventroni, Guido, Baugh, Dainia, Madu, Ernest, Aikawa, Tadao, Asano, Hiroshi, Fujimoto, Shinichiro, Fujise, Koichiro, Fukushima, Yoshimitsu, Fukuyama, Kae, Ichikawa, Yasutaka, Ideguchi, Reiko, Iguchi, Nobuo, Imai, Masamichi, Ishimura, Hayato, Isobe, Satoshi, Ito, Kimiteru, Izawa, Yu, Kadokami, Toshiaki, Kasai, Tokuo, Kato, Takao, Kawamoto, Takashi, Kiryu, Shigeru, Kumita, Shinichiro, Manabe, Osamu, Maruno, Hirotaka, Matsumoto, Naoya, Miyagawa, Masao, Moroi, Masao, Nagamachi, Shigeki, Nakajima, Kenichi, Nakazato, Ryo, Nanasato, Mamoru, Naya, Masanao, Norikane, Takashi, Ohta, Yasutoshi, Otomi, Yoichi, Otsuka, Hideki, Oyama-Manabe, Noriko, Saito, Masaki, Sarai, Masayoshi, Sato, Junichi, Sato, Daisuke, Shiraishi, Shinya, Takanami, Kentaro, Takehana, Kazuya, Taniguchi, Yasuyo, Teragawa, Hiroki, Tomizawa, Nobuo, Umeji, Kyoko, Wakabayashi, Yasushi, Yamada, Shinichiro, Yamazaki, Shinya, Yoneyama, Tatsuya, Rawashdeh, Mohammad, Dautov, Tairkhan, Makhdomi, Khalid, Abass, Mostafa, Garashi, Masoud, Siraj, Qaisar, Kalnina, Marika, Haidar, Mohamad, Komiagiene, Renata, Kviecinskiene, Giedre, Vajauskas, Donatas, Karim, Noor Khairiah A., Doucoure, Mady, Reichmuth, Luise, Samuel, Anthony, Dieng, Mohamed Lemine, Naojee, Ambedhkar Shantaram, Hernandez, Estrella Aguilera, Alducin Tellez, Cesar Rene, Alexánderson-Rosas, Erick, Barragan, Erika, Cabada, Manuel, Calderón, Daniel, Carvajal-Juarez, Isabel, Esparza, José, Gama-Moreno, Manlio Gerardo, Quinto, Virginia Garcia, Gonzalez, Nelsy Coromoto, Herrera-Zarza, Mary Carmen, Meave, Aloha, Medina Verdugo, Jesus Gregorio, Melendez, Gabriela, Morales Murguia, Rafael Humberto, Navarro Quiroz, Carlos Salvador, Ornelas, Mario, Preciado-Anaya, Andres, Preciado-Gutiérrez, Oscar Ulises, Puente, Adriana, Salazar, Aristóteles Ramírez, Rosales Uvera, Sandra Graciela, Rosales-Uvera, Sandra, Serna Macias, Jose Antonio, Sierra-Galan, Lilia, Sierra-Galan, Lilia M., Tirado Alderete, Juan Carlos, Vallejo, Enrique, Faraggi, Marc, Sereegotov, Erdenechimeg, Ben Rais, Nouzha, Alaoui, Nadia Ismaili, Kyiphyu, Thiri, Oo, Su Thet, Win, Soe Myat, Zar, Htin, Ghimire, Ram, Neupane, Madhu, Glaudemans, Andor, Slart, Riemer, Verschure, Derk, Allen, Berry, Edmond, John, Mckenzie, Clare, Tie, Stuart, Van Pelt, Niels, Worthington, Kirsten, Young, Calum, Soli, Idrissa Adamou, Kana, Shehu, Onubogu, Uchenna, Sani, Mahmoud, Bråten, Anders Tjellaug, Jørgensen, Arve, Vassbotn, Hanne-Elin, Al Dhuhli, Humoud, Jawa, Zabah, Tag, Naima, Fatima, Shazia, Imran, Muhammad Babar, Younis, Muhammad Numair, Saadullah, Mohammad, Malo, Yariela Herrera, Lenturut-Katal, Dora, Castillo, Manuel, Ortellado, José, Akhter, Afroza, Cader, F. Aaysha, Hussain, Raihan, Khan, Saidur Rahman, Mandal, Tapati, Nasreen, Faria, An, Yunqiang, Cao, Dianbo, Gong, Lianggeng, Hou, Yang, Jia, Chongfu, Li, Tao, Li, Caiying, Liu, Hui, Liu, Wenya, Liu, Jinkang, Lu, Bin, Ng, Ming-Yen, Shi, Heshui, Tang, Chunxiang, Wang, Ximing, Wang, Zhaoqian, Wang, Yining, Wu, Jiang, Yi, Yan, Yuan, Li, Zhang, Tong, Zhang, Longjiang, Chavez, Edith, Cruz, Carlos, Llontop, Christian, Morales, Rosanna, Abrihan, Paz, Bustos-Barroso, Asela, Duldulao-Ogbac, Michele, Eduarte, Christopher, Obaldo, Jerry, Quinon, Alvin, San Juan, Belinda, San Juan, Carlo Joe, Sauler-Gomez, Marie Rhiamar, Uy, Mila, Kostkiewicz, Magdalena, Kunikowska, Jolanta, Teresinska, Anna, Urbanik, Tomasz, Bettencourt, Nuno, Fontes-Carvalho, Ricardo, Gavina, Cristina, Gonçalves, Lino, Macedo, Filipe, Moreno, Nuno, Sousa, Carla, Timoteo, Ana Teresa, Vidigal, Maria João, Al Heidous, Mahmoud, Ramanathan, Subramaniyan, Arnous, Samer, Aytani, Said, Byrne, Angela, Gleeson, Tadhg, Kerins, David, O'Brien, Julie, Bang, Ji-In, Bom, Henry, Cheon, Miju, Cheon, Gi Jeong, Cho, Sang-Geon, Hong, Chae Moon, Jeong, Yong Hyu, Kang, Won Jun, Kang, Yeon-Koo, Kim, Ji-Young, Oh, So Won, So, Young, Song, Ho-Chun, Won, Kyoung Sook, Yoo, Soo Woong, Mitevska, Irena, Vavlukis, Marija, Salobir, Barbara Gužic, Štalc, Monika, Benedek, Theodora, Pop, Marian, Stan, Claudiu, Ansheles, Alexey, Dariy, Olga, Gagarina, Nina, Itskovich, Irina, Karalkin, Anatoliy, Kokov, Alexander, Marina, Gulya, Migunova, Ekaterina, Pospelov, Viktor, Ryzhkova, Daria, Sayfullina, Guzaliya, Sergienko, Vladimir, Shurupova, Irina, Sinitsyn, Valentin, Vakhromeeva, Margarita, Valiullina, Nailia, Zavadovsky, Konstantin, Zhuravlev, Kirill, Abazid, Rami, Al Garni, Turki, Alasnag, Mirvat, Aljizeeri, Ahmed, Amer, Hamid, Amro, Ahmad, Hamdy, Hesham, Smettei, Osama, Saranovic, Dragana Sobic, Vlajkovic, Marina, Keng, Felix, See, Jason, Berecova, Zuzana, Mistinova, Jana Polakova, Evbuomwan, Osayande, Govender, Nerisha, Hack, Jonathan, Hadebe, Bawinile, Hlongwa, Khanyisile, Kaplan, Mitchell, Lakhi, Hoosen, Milos, Katarina, Modiselle, Moshe, More, Stuart, Muambadzi, Ntanganedzeni, Scholtz, Leonie, Barreiro-Perez, Manuel, Blanco, Isabel, Broncano, Jordi, Camarero, Alicia, Casáns-Tormo, Irene, De Haro, Javier, Flotats, Albert, García, Elia, Mendiguchia, Ceferino Gutierrez, Jimenez-Heffernan, Amelia, Leta, Ruben, Diaz, Javier Lopez, Vega, Luis Lumbreras, Manovel-Sánchez, Ana, Monzonis, Amparo Martinez, Patrut, Bianca, Pubul, Virginia, Perez, Ricardo Ruano, Zeidan, Nahla, Nanayakkara, Damayanthi, Suliman, Ahmed, Engblom, Henrik, Murtadha, Mustafa, Ostenfeld, Ellen, Simonsson, Magnus, Alkadhi, Hatem, Buechel, Ronny Ralf, Burger, Peter, Gräni, Christoph, Kamani, Christel, Kawel-Böhm, Nadine, Klaeser, Bernd, Manka, Robert, Prior, John, Kaewchur, Tawika, Khiewvan, Benjapa, Kositwattanarerk, Arpakorn, Namwongprom, Sirianong, Thientunyakit, Tanyaluck, Sayman, Haluk Burcak, Yüksel, Mahmut, Sebikali, Mugisha Julius, Okello, Emmy, Korol, Pavlo, Noverko, Iryna, Satyr, Maryna, Ahmad, Tahir, Alfakih, Khaled, Andrade, Ivo, Buckingham, Susan, Bularga, Anda, Carpenter, John-Paul, Cole, Graham, Cusack, David, David, Sarojini, Davis, Patrick, Fairbairn, Timothy, Ghosh, Arjun, Ramkumar, Prasad Guntur, Hamilton, Mark, Haque, Faisal, Hudson, Benjamin, Johnstone, Annette, Karthikeyan, V.J., Kay, Mike, Khan, Mohammad Ali, Kitt, Jamie, Low, Chen Sheng, Mcalindon, Elisa, Mccreavy, David, Morrissey, Brian, Motwani, Manish, Na, Dilip, Nicol, Edward, Patel, Dilip, Rodrigues, Jonathan, Rofe, Chris, Schofield, Rebecca, Semple, Thomas, Sheikh, Azeem, Sinha, Apurva, Subedi, Deepak, Topping, William, Tweed, Katherine, Underwood, Stephen Richard, Weir-Mccall, Jonathan, Zuhairy, Hamed, Abbasi, Taimur, Abohashem, Shady, Abramson, Sandra, Al-Mallah, Mouaz, Kumar, Mohan Ashok, Balmer-Swain, Mallory, Berman, Daniel, Bernheim, Adam, Bhatti, Sabha, Biederman, Robert, Bieging, Erik, Bingham, Scott, Bloom, Stephen, Blue, Sean, Borges, Andressa, Branch, Kelley, Bravo, Paco, Buddhe, Sujatha, Budoff, Matthew, Bullock-Palmer, Renée, Cahill, Michael, Candela, Candace, Cao, Jane, Chatterjee, Saurav, Chatzizisis, Yiannis, Chaudhuri, Nita Ray, Cheezum, Michael, Chelliah, Anjali, Chen, Tiffany, Chen, Marcus, Chen, Lu, Chokshi, Aalap, Chung, Jina, Danciu, Sorin, DeSisto, William, Dilorenzo, Michael, Doukky, Rami, Duvall, William, Ferencik, Maros, Foster, Cameron, Fuisz, Anthon, Gannon, Michael, German, David, Gerson, Myron, Geske, Jeffrey, Hage, Fadi, Haider, Agha, Haider, Sofia, Hamirani, Yasmin, Hassen, Karen, Hendel, Robert, Henkel, Jacqueline, Horgan, Stephen, Hyun, Mark, Janardhanan, Rajesh, Jerome, Scott, Kalra, Dinesh, Kassop, David, Kinkhabwala, Mona, Kinzfogl, George, Koch, Bernard, Koweek, Lynne, Krepp, Joseph, Kwon, Younghoon, Layer, Jay, Lesser, John, Leung, Steve, Lisske, Bernadette, Magurany, Kathleen, Markowitz, Jeremy, Mccullough, Brenda, Moalemi, Azita, Moffitt, Chanan, Montanez, Juan, Moore, Warren, Morayati, Shamil, Mossa-Basha, Mahmud, Mrsic, Zorana, Murthy, Venkatesh, Nagpal, Prashant, Nelson, Katarina, Nijjar, Prabhjot, O’Quinn, Rupal, Passen, Edward, Patel, Toral, Patil, Pravin, Pursnani, Amit, Quachang, Nancy, Rabbat, Mark, Ranjan, Pragya, Lozano, Patricia Rodriguez, Schemmer, Mary, Seifried, Rebecca, Shah, Nishant, Shah, Amee, Shanbhag, Sujata, Sharma, Gaurav, Skotnicki, Robert, Sobczak, Michael, Soman, Prem, Sorrell, Vincent, Srichai, Monvadi, Streeter, Jim, Strickland, Leah, Suliman, Suliman, Tebyanian, Naghmeh, Thomas, Dustin, Thompson, Randall, Uretsky, Seth, Vallurupalli, Srikanth, Vandyck-Acquah, Marian, Verma, Vikas, Villines, Todd, Weinstein, Joseph, Wolinsky, David, Zareba, Karolina, Zgaljardic, Michael, Beretta, Mario, Ferrando, Rodolfo, Kapitan, Miguel, Mut, Fernando, Djuraev, Omoa, Rozikhodjaeva, Gulnora, Vera, Luisa, Duc, Binh Duong, Nguyen, Xuan Canh, Hiep Nguyen, Phuoc Minh, Hirschfeld, Cole, Choi, Andrew D., Ansheles, Alexey A., Kudo, Takashi, Bucciarelli-Ducci, Chiara, Nørgaard, Bjarne Linde, Maurovich-Horvat, Pál, Louw, Lizette, Allam, Adel H., and Narula, Jagat

Published
2022
Full Text
View/download PDF

49. Cardiac Complications in Patients Hospitalised With COVID-19 in Australia

Author

Bhatia, Kunwardeep S., Sritharan, Hari P., Chia, Justin, Ciofani, Jonathan, Nour, Daniel, Chui, Karina, Vasanthakumar, Sheran, Jayadeva, Pavithra, Kandadai, Dhanvee, Allahwala, Usaid, Bhagwandeen, Rohan, Brieger, David B., Choong, Christopher Y.P., Delaney, Anthony, Dwivedi, Girish, Harris, Benjamin, Hillis, Graham, Hudson, Bernard, Javorsky, George, Jepson, Nigel, Kanagaratnam, Logan, Kotsiou, George, Lee, Astin, Lo, Sidney T.H., MacIsaac, Andrew I., McQuillan, Brendan M., Ranasinghe, Isuru, Walton, Antony, Weaver, James, Wilson, William, Yong, Andy, Zhu, John, van Gaal, William, Kritharides, Leonard, Chow, Clara, and Bhindi, Ravinay

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results