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Chandra Prakash Yadav

National University of Singapore

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Scholarly Papers (1)

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Interactive Pathways of Key Prognostic Factors Leading to Severe Asthma Exacerbations: A Bayesian Network Comparison between Clinical Trials and Real-World Registries

Number of pages: 40 Posted: 24 Dec 2024
National University of Singapore, National University of Singapore, University of Aberdeen - Centre of Academic Primary CareObservational and Pragmatic Research Institute Pte Ltd (OPRI), Duke-NUS Medical School, National University of Singapore, Medical Research Institute of New Zealand, Uppsala University, National University of Singapore (NUS) - Singapore General Hospital, University of Colorado, Aurora, National Jewish Health, King’s College London, Queen's University Belfast, Queen's University Belfast, Queen Mary University of London - Institute of Population Health Sciences, University of Sharjah, Taipei Veterans General Hospital, Hospital Universitari Son Espases, Hospital Universitario Lucus Augusti (HULA), King Fahad Medical City, Médica Sur Clinical Foundation and Hospital - Center of Excellence in Asthma and Allergy, Department of Communication Disorders Sciences, Catholic University of Korea, Kindai University - Kindai University Hospital, Humanitas University, Royal College of Surgeons in Ireland (RCSI), Royal College of Surgeons in Ireland (RCSI), University of Athens - Allergy Department, University of Athens, Bispebjerg and Frederiksberg Hospitals - Department of Respiratory Medicine and Infectious Diseases, Colombian Pulmonology Foundation, Medical University of Sofia, University Hospital “Sv. Ivan Rilski”, Monash University, The University of Sydney - Concord Hospital, University of Newcastle, University of Buenos Aires (UBA), Vancouver Coastal Health Research Institute, Secretary of National Defense, Institute of Security and Social Services for State Workers and National University of Singapore
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Abstract:

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Severe asthma, risk prediction, Causal inference, Bayesian network, Randomized controlled trails, Real world data