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Mehrdad A. Mizani

University College London - Institute of Health Informatics

United Kingdom

  • University College London - Institute of Health Informatics

SCHOLARLY PAPERS

6

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1,793

TOTAL CITATIONS

7

Scholarly Papers (6)

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1.

Healthcare Utilisation of 282,080 Individuals with Long COVID Over Two Years: A Multiple Matched Control Cohort Analysis

Number of pages: 29 Posted: 19 Oct 2023
Yi Mu, Ashkan Dashtban, Mehrdad A. Mizani, Christopher Tomlinson, Mohamed Mohamed, Mark Ashworth, Mamas Mamas, Rouven Priedon, Steffen E. Petersen, Evan Kontopantelis, Christina Pagel, Mevhibe Hocaoglu, Kamlesh Khunti, Johan Hilge Thygesen, Paula Lorgelly, Manuel Gomes, Melissa Heightman, Amitava Banerjee and Richard Williams
University College London, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London, King’s College London - ESRC Centre for Society and Mental Health, Keele University - Research Institute for Primary Care and Health Sciences, Health Data Research UK, Government of the United Kingdom - Barts Health NHS Trust, The University of Manchester, University College London, University of London, Kings College London, African Leadership Centre, Students, University of Leicester - Leicester Diabetes Centre, University College London - Institute of Health Informatics, University of Auckland, University College London, University College London - University College London Hospitals NHS Foundation Trust, University College London - Institute of Health Informatics and University College London
Downloads 1,280 (39,698)
Citation 7

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Long COVID, COVID, healthcare utilisation, cost, health policy

2.

Using National Electronic Health Records for Pandemic Preparedness: Validation of a Parsimonious Model for Predicting Excess Deaths Among Those With COVID-19

Downloads 165 (454,356)

Using National Electronic Health Records for Pandemic Preparedness: Validation of a Parsimonious Model for Predicting Excess Deaths Among Those With COVID-19

Number of pages: 19 Posted: 08 Mar 2022
Mehrdad A. Mizani, Ashkan Dashtban, Laura Pasea, Alvina Lai, Johan Hilge Thygesen, Christopher Tomlinson, Alex Handy, Jil Billy Mamza, Tamsin Morris, Sara Khalid, Francesco Zaccardi, Mary J. Macleod, Fatemeh Torabi, Dexter Canoy, Ashley Akbari, Colin Berry, Thomas Bolton, John Nolan, Kamlesh Khunti, Spiros Denaxas, Harry Hemingway, Cathie Sudlow, Amitava Banerjee and CVD-COVID-UK Consortium
University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, University of Oxford - Rheumatology and Musculoskeletal Sciences, University of Leicester - Leicester Diabetes Centre, University of Aberdeen - Medical Sciences and Nutrition, Swansea University - Population Data Science, University of Oxford - Nuffield Department of Women’s and Reproductive Health, Swansea University - Population Data Science, University of Glasgow - Institute of Cardiovascular and Medical Sciences, Health Data Research UK - BHF Data Science Centre, Health Data Research UK - BHF Data Science Centre, University of Leicester - Leicester Diabetes Centre, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, Health Data Research UK - BHF Data Science Centre, University College London - Institute of Health Informatics and affiliation not provided to SSRN
Downloads 108 (653,927)

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coronavirus, excess mortality, risk prediction, health policy, pandemic preparedness

Using National Electronic Health Records for Pandemic Preparedness: Validation of a Parsimonious Model for Predicting Excess Deaths Among Those With COVID-19

Number of pages: 20 Posted: 25 Mar 2022
Mehrdad A. Mizani, Muhammad H Dashtban, Laura Pasea, Alvina G Lai, Johan Hilge Thygesen, Christopher Tomlinson, Alex Handy, Jil Billy Mamza, Tamsin Morris, Sara Khalid, Francesco Zaccardi, Mary Joan Macleod, Fatemeh Torabi, Dexter Canoy, Ashley Akbari, Colin Berry, Thomas Bolton, John Nolan, Kamlesh Khunti, Spiros Denaxas, Harry Hemingway, Cathie Sudlow, Amitava Banerjee and CVD-COVID-UK Consortium
University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, University of Oxford - Rheumatology and Musculoskeletal Sciences, University of Leicester - Leicester Diabetes Centre, University of Aberdeen - Medical Sciences and Nutrition, Swansea University - Population Data Science, University of Oxford - Nuffield Department of Women’s and Reproductive Health, Swansea University - Population Data Science, University of Glasgow - Institute of Cardiovascular and Medical Sciences, Health Data Research UK - BHF Data Science Centre, Health Data Research UK - BHF Data Science Centre, University of Leicester - Leicester Diabetes Centre, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, Health Data Research UK - BHF Data Science Centre, University College London - Institute of Health Informatics and Independent
Downloads 57 (1,006,630)

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3.

Association of COVID-19 and Influenza Vaccinations and Cardiovascular Pharmacotherapy with Hospitalisation and Mortality in People with COVID-19 and Long COVID: 2-Year Follow-Up of Over 17 Million People in England

Number of pages: 33 Posted: 14 Nov 2023
Ashkan Dashtban, Mehrdad A. Mizani, Laura Pasea, Christopher Tomlinson, Yi Mu, Nazrul Islam, Sarah Rafferty, Charlotte Warren-Gash, Spiros Denaxas, Kim Horstmanshof, Evan Kontopantelis, Steffen E. Petersen, Cathie Sudlow, Kamlesh Khunti, Amitava Banerjee and CVD-COVID-UK Consortium
University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London, University of Southampton, Government of the United Kingdom - Office for National Statistics, London School of Hygiene & Tropical Medicine - Faculty of Epidemiology and Population Health, University College London - Institute of Health Informatics, University College London, The University of Manchester, Government of the United Kingdom - Barts Health NHS Trust, Health Data Research UK, University of Leicester - Leicester Diabetes Centre, University College London - Institute of Health Informatics and Independent
Downloads 110 (640,165)

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COVID-19, Long COVID, cardiovascular disease, vaccination, high-risk

4.

Predicting and Validating Risk of Pre-Pandemic and Excess Mortality in Individuals With Chronic Kidney Disease

Number of pages: 21 Posted: 24 Nov 2021
Ashkan Dashtban, Mehrdad A. Mizani, Spiros Denaxas, Dorothea Nitsch, Jennifer Quint, Richard Corbett, Jil Billy Mamza, Tamsin Morris, Mamas Mamas, Deborah A. Lawlor, Kamlesh Khunti, Rouven Priedon, Cathie Sudlow, Harry Hemingway and Amitava Banerjee
University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, London School of Hygiene & Tropical Medicine - Department of Non-Communicable Disease Epidemiology, Imperial College London - National Heart and Lung Institute, Imperial College London, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, Keele University - Research Institute for Primary Care and Health Sciences, University of Bristol - Department of Population Health Sciences, University of Leicester - Leicester Diabetes Centre, Health Data Research UK, Health Data Research UK - BHF Data Science Centre, University College London - Institute of Health Informatics and University College London - Institute of Health Informatics
Downloads 107 (654,668)

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chronic kidney disease, multimorbidity, excess mortality, coronavirus, risk prediction

5.

Risk Factors, Outcomes and Healthcare Utilisation in Individuals with Multimorbidity Including Heart Failure, Chronic Kidney Disease and Type 2 Diabetes Mellitus - a National Electronic Health Record Study

Number of pages: 23 Posted: 08 Aug 2022
Laura Pasea, Mehrdad A. Mizani, Ashkan Dashtban, Anish Bhuva, Tamsin Morris, Jil Billy Mamza and Amitava Banerjee
University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs and University College London - Institute of Health Informatics
Downloads 76 (830,555)

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multimorbidity, heart failure, chronic kidney disease, diabetes, healthcare utilisation

6.

Identifying Subtypes of Chronic Kidney Disease with Machine Learning: Development, Internal Validation and Prognostic Validation Using Electronic Health Records in 350067 Individuals

Number of pages: 19 Posted: 27 Oct 2022
Ashkan Dashtban, Mehrdad A. Mizani, Laura Pasea, Spiros Denaxas, Richard Corbett, Jil Billy Mamza, He Gao, Tamsin Morris, Harry Hemingway and Amitava Banerjee
University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, University College London - Institute of Health Informatics, Imperial College London - Imperial College Healthcare NHS Trust, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca - Medical and Scientific Affairs, University College London - Institute of Health Informatics and University College London - Institute of Health Informatics
Downloads 55 (1,010,164)

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chronic kidney disease, cluster analysis, machine learning, unsupervised clustering, survival analysis.