Usman Farooqui

494 total citations
17 papers, 310 citations indexed

About

Usman Farooqui is a scholar working on Cardiology and Cardiovascular Medicine, Economics and Econometrics and Epidemiology. According to data from OpenAlex, Usman Farooqui has authored 17 papers receiving a total of 310 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cardiology and Cardiovascular Medicine, 6 papers in Economics and Econometrics and 3 papers in Epidemiology. Recurrent topics in Usman Farooqui's work include Atrial Fibrillation Management and Outcomes (12 papers), Health Systems, Economic Evaluations, Quality of Life (6 papers) and Cardiac Arrhythmias and Treatments (5 papers). Usman Farooqui is often cited by papers focused on Atrial Fibrillation Management and Outcomes (12 papers), Health Systems, Economic Evaluations, Quality of Life (6 papers) and Cardiac Arrhythmias and Treatments (5 papers). Usman Farooqui collaborates with scholars based in United Kingdom, France and Pakistan. Usman Farooqui's co-authors include Nathan R. Hill, Jason Gordon, Belinda Sandler, Steven Lister, Alexander T. Cohen, Frank Xiaoqing Liu, David A. Clifton, Phil McEwan, Daniel Sugrue and Ameet Bakhai and has published in prestigious journals such as PLoS ONE, The American Journal of Cardiology and Value in Health.

In The Last Decade

Usman Farooqui

16 papers receiving 305 citations

Peers

Usman Farooqui
Claudia Amato Germany
Eilon Gabel United States
Julian S. Haimovich United States
Belinda Sandler United Kingdom
José Castela Forte Netherlands
Simrat Gill United Kingdom
Otilia Țica United Kingdom
Louis Lind Plesner Denmark
Nicoletta Musacchio Italy
Alana Lewis United States
Claudia Amato Germany
Usman Farooqui
Citations per year, relative to Usman Farooqui Usman Farooqui (= 1×) peers Claudia Amato

Countries citing papers authored by Usman Farooqui

Since Specialization
Citations

This map shows the geographic impact of Usman Farooqui's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Usman Farooqui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Usman Farooqui more than expected).

Fields of papers citing papers by Usman Farooqui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Usman Farooqui. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Usman Farooqui. The network helps show where Usman Farooqui may publish in the future.

Co-authorship network of co-authors of Usman Farooqui

This figure shows the co-authorship network connecting the top 25 collaborators of Usman Farooqui. A scholar is included among the top collaborators of Usman Farooqui based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Usman Farooqui. Usman Farooqui is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Farooqui, Usman, et al.. (2024). Cutaneous signs of selected cardiovascular disorders: A narrative review. Open Medicine. 19(1). 20240897–20240897. 1 indexed citations
2.
Lawton, Sarah, Daniel Sugrue, Chris Arden, et al.. (2024). Undertaking multi-centre randomised controlled trials in primary care: learnings and recommendations from the PULsE-AI trial researchers. BMC Primary Care. 25(1). 7–7.
3.
Tahir, Muhammad Junaid, Muhammad Sohaib Asghar, Faizan Ahmed, et al.. (2023). Knowledge, attitudes, and practices of the general population of Pakistan regarding typhoid conjugate vaccine: findings of a cross-sectional study. Frontiers in Public Health. 11. 1151936–1151936. 2 indexed citations
4.
5.
Szymański, Tomasz, et al.. (2022). Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients. EP Europace. 24(8). 1240–1247. 20 indexed citations
6.
Mamas, Mamas A., Sarah Batson, Andrew Matthew, et al.. (2021). Meta-Analysis Comparing Apixaban Versus Rivaroxaban for Management of Patients With Nonvalvular Atrial Fibrillation. The American Journal of Cardiology. 166. 58–64. 10 indexed citations
7.
Gordon, Jason, Thomas Mason, Belinda Sandler, et al.. (2021). Using machine learning to predict anticoagulation control in atrial fibrillation: A UK Clinical Practice Research Datalink study. Informatics in Medicine Unlocked. 25. 100688–100688. 9 indexed citations
8.
Pollock, Kevin G., Belinda Sandler, Nathan R. Hill, et al.. (2020). Application of a machine learning algorithm for detection of atrial fibrillation in secondary care. IJC Heart & Vasculature. 31. 100674–100674. 9 indexed citations
9.
Hill, Nathan R., Chris Arden, A. John Camm, et al.. (2020). Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial. Contemporary Clinical Trials. 99. 106191–106191. 16 indexed citations
10.
Hill, Nathan R., Belinda Sandler, Dušan Milenković, et al.. (2020). A Systematic Review of Network Meta-Analyses and Real-World Evidence Comparing Apixaban and Rivaroxaban in Nonvalvular Atrial Fibrillation. Clinical and Applied Thrombosis/Hemostasis. 26. 2873297292–2873297292. 17 indexed citations
11.
Sandler, Belinda, Kevin G. Pollock, Nathan R. Hill, et al.. (2020). Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study. European Journal of Preventive Cardiology. 28(6). 598–605. 39 indexed citations
12.
Hill, Nathan R., Daniel Ayoubkhani, Phil McEwan, et al.. (2019). Predicting atrial fibrillation in primary care using machine learning. PLoS ONE. 14(11). e0224582–e0224582. 90 indexed citations
13.
Hill, Nathan R., Belinda Sandler, Steven Lister, et al.. (2019). Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm. Journal of Medical Economics. 23(4). 386–393. 29 indexed citations
14.
Ayoubkhani, Daniel, Matthew Lumley, Steven Lister, et al.. (2018). Machine Learning to Detect and Diagnose Atrial Fibrillation and Atrial Flutter (AF/F) Using Routine Clinical Data. Value in Health. 21. S213–S213. 1 indexed citations
15.
Hill, Nathan R., Daniel Ayoubkhani, Phil McEwan, et al.. (2018). Predicting Atrial Fibrillation in Primary Care Using Machine Learning. SSRN Electronic Journal. 4 indexed citations
16.
Liu, Frank Xiaoqing, et al.. (2014). Peritoneal Dialysis and In-Centre Haemodialysis: A Cost-Utility Analysis from a UK Payer Perspective. Applied Health Economics and Health Policy. 12(4). 409–420. 49 indexed citations
17.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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