Anoop D Shah
About
Anoop D Shah is a scholar working on Cardiology and Cardiovascular Medicine, Artificial Intelligence and Molecular Biology.
According to data from OpenAlex, Anoop D Shah has authored 68 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Cardiology and Cardiovascular Medicine, 15 papers in Artificial Intelligence and 13 papers in Molecular Biology. Recurrent topics in Anoop D Shah's work include Machine Learning in Healthcare (12 papers), Biomedical Text Mining and Ontologies (12 papers) and Blood Pressure and Hypertension Studies (9 papers). Anoop D Shah is often cited by papers focused on Machine Learning in Healthcare (12 papers), Biomedical Text Mining and Ontologies (12 papers) and Blood Pressure and Hypertension Studies (9 papers). Anoop D Shah collaborates with scholars based in United Kingdom, Netherlands and United States. Anoop D Shah's co-authors include Harry Hemingway, Spiros Denaxas, Adam Timmis, Liam Smeeth, Eleni Rapsomaniki, Mar Pujades‐Rodríguez, John Deanfield, Julie George, Aroon D. Hingorani and Owen Nicholas and has published in prestigious journals such as The Lancet, Circulation and SHILAP Revista de lepidopterología.
In The Last Decade
Anoop D Shah
62 papers receiving 5.4k citations
Hit Papers
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late) cites · hero ref
| Name | h | Career | Trend | Papers | Cites | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Anoop D Shah United Kingdom | 29 | 2.1k | 900 | 895 | 648 | 594 | 68 | 5.5k | ||
| Spiros Denaxas United Kingdom | 40 | 2.7k 1.3× | 1.1k 1.2× | 1.3k 1.4× | 811 1.3× | 787 1.3× | 186 | 7.7k | ||
| Girish N. Nadkarni United States | 44 | 1.3k 0.6× | 680 0.8× | 950 1.1× | 999 1.5× | 752 1.3× | 348 | 7.7k | ||
| Rohan Khera United States | 45 | 2.7k 1.3× | 644 0.7× | 1.1k 1.2× | 1.2k 1.9× | 384 0.6× | 263 | 7.0k | ||
| Mouaz H. Al‐Mallah United States | 44 | 3.9k 1.9× | 645 0.7× | 775 0.9× | 1.1k 1.7× | 404 0.7× | 347 | 7.7k | ||
| Navdeep Tangri Canada | 48 | 2.1k 1.0× | 851 0.9× | 804 0.9× | 1.1k 1.7× | 480 0.8× | 351 | 8.7k | ||
| Ann Marie Návar United States | 37 | 1.4k 0.7× | 857 1.0× | 920 1.0× | 1.8k 2.8× | 280 0.5× | 167 | 5.4k | ||
| Dexter Canoy United Kingdom | 37 | 1.1k 0.5× | 516 0.6× | 688 0.8× | 346 0.5× | 237 0.4× | 82 | 4.2k | ||
| Yong Jiang China | 31 | 1.1k 0.5× | 489 0.5× | 2.1k 2.3× | 482 0.7× | 618 1.0× | 186 | 7.3k | ||
| Michael E. Matheny United States | 52 | 1.2k 0.5× | 760 0.8× | 1.9k 2.2× | 1.0k 1.6× | 909 1.5× | 265 | 9.7k | ||
| Emily Herrett United Kingdom | 22 | 1.1k 0.5× | 545 0.6× | 1.0k 1.2× | 728 1.1× | 293 0.5× | 43 | 5.2k |
Countries citing papers authored by Anoop D Shah
Since
Specialization
Citations
This map shows the geographic impact of Anoop D Shah'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 Anoop D Shah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anoop D Shah more than expected).
Fields of papers citing papers by Anoop D Shah
Since
Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences
This network shows the impact of papers produced by Anoop D Shah. 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 Anoop D Shah. The network helps show where Anoop D Shah may publish in the future.
Co-authorship network of co-authors of Anoop D Shah
This figure shows the co-authorship network connecting the top 25 collaborators of Anoop D Shah. A scholar is included among the top collaborators of Anoop D Shah 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 Anoop D Shah. Anoop D Shah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
1.
Ross, Jack, Yogini Jani, Enrico Costanza, et al.. (2025). Design and implementation of a natural language processing system at the point of care: MiADE (medical information AI data extractor). BMC Medical Informatics and Decision Making. 25(1). 365–365.
2.
Shah, Anoop D, et al.. (2025). Impact of Non-Tobacco Nicotine Dependence on Outcomes Following Anterior Lumbar Interbody Fusion. Spine.
3.
Chen, Yang, et al.. (2023). Digital technology and patient and public involvement (PPI) in routine care and clinical research—A pilot study. PLoS ONE. 18(2). e0278260–e0278260.
4.
Roguski, Łukasz, Xi Bai, Álex Handy, et al.. (2022). Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals. JMIR Medical Informatics. 10(8). e38122–e38122.
5.
Patel, Riyaz, Spiros Denaxas, Laurence J Howe, et al.. (2022). Reproducible disease phenotyping at scale: Example of coronary artery disease in UK Biobank. PLoS ONE. 17(4). e0264828–e0264828.
6.
Shah, Anoop D, et al.. (2021). Data gaps in electronic health record (EHR) systems: An audit of problem list completeness during the COVID-19 pandemic. International Journal of Medical Informatics. 150. 104452–104452.
7.
Denaxas, Spiros, Anoop D Shah, Bilal A. Mateen, et al.. (2020). A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems. JAMIA Open. 3(4). 545–556.
8.
Shah, Anoop D, David Brealey, Steve Harris, et al.. (2020). Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial. IEEE Journal of Biomedical and Health Informatics. 24(10). 2950–2959.
9.
Li, Qianrui, Xiaodan Li, Jing Wang, et al.. (2019). Diagnosis and treatment for hyperuricemia and gout: a systematic review of clinical practice guidelines and consensus statements. BMJ Open. 9(8). e026677–e026677.
10.
Shah, Anoop D, Emily S. Bailey, Tim Williams, et al.. (2019). Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death. Journal of Biomedical Semantics. 10(S1). 20–20.
11.
Velupillai, Sumithra, Hanna Suominen, Maria Liakata, et al.. (2018). Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances. Journal of Biomedical Informatics. 88. 11–19.
12.
Hemingway, Harry, Gene Feder, Natalie Fitzpatrick, et al.. (2017). Using nationwide ‘big data’ from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using LInked Bespoke studies and Electronic health Records (CALIBER) programme. SHILAP Revista de lepidopterología. 5(4). 1–330.
13.
Timmis, Adam, Eleni Rapsomaniki, Sheng‐Chia Chung, et al.. (2016). Prolonged dual antiplatelet therapy in stable coronary disease: comparative observational study of benefits and harms in unselected versus trial populations. BMJ. 353. i3163–i3163.
14.
Allan, Victoria, Amitava Banerjee, Anoop D Shah, et al.. (2016). Net clinical benefit of warfarin in individuals with atrial fibrillation across stroke risk and across primary and secondary care. Heart. 103(3). 210–218.
15.
Rapsomaniki, Eleni, Adam Timmis, Julie George, et al.. (2014). Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1·25 million people. The Lancet. 383(9932). 1899–1911.
16.
Pujades‐Rodríguez, Mar, Julie George, Anoop D Shah, et al.. (2014). Heterogeneous associations between smoking and a wide range of initial presentations of cardiovascular disease in 1 937 360 people in England: lifetime risks and implications for risk prediction. International Journal of Epidemiology. 44(1). 129–141.
17.
George, Julie, Emily Herrett, Spiros Denaxas, et al.. (2012). Abstract 15464: Differential Effects of Smoking on Specific Cardiovascular Presentations in Men and Women: Prospective Cohort Study in 900,000 Patients Using CALIBER Linked Electronic Health Records. Circulation. 126.
18.
Shah, Anoop D, Owen Nicholas, Adam Timmis, et al.. (2011). Threshold Haemoglobin Levels and the Prognosis of Stable Coronary Disease: Two New Cohorts and a Systematic Review and Meta-Analysis. PLoS Medicine. 8(5). e1000439–e1000439.
19.
Wang, Zhuoran, John Shawe‐Taylor, & Anoop D Shah. (2010). Semi-supervised feature learning from clinical text. 3. 462–466.
20.
Shah, Anoop D, et al.. (2005). Gastroesophageal Reflux Disease and Obesity. Gastroenterology Clinics of North America. 34(1). 35–43.
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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