Thomas Falconer

1.1k total citations
11 papers, 67 citations indexed

About

Thomas Falconer is a scholar working on Epidemiology, Statistics and Probability and Molecular Biology. According to data from OpenAlex, Thomas Falconer has authored 11 papers receiving a total of 67 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Epidemiology, 3 papers in Statistics and Probability and 2 papers in Molecular Biology. Recurrent topics in Thomas Falconer's work include Machine Learning in Healthcare (2 papers), Liver Disease Diagnosis and Treatment (2 papers) and Liver Disease and Transplantation (2 papers). Thomas Falconer is often cited by papers focused on Machine Learning in Healthcare (2 papers), Liver Disease Diagnosis and Treatment (2 papers) and Liver Disease and Transplantation (2 papers). Thomas Falconer collaborates with scholars based in United States, South Korea and Germany. Thomas Falconer's co-authors include George Hripcsak, Nigam H. Shah, Patrick Ryan, Juan M. Banda, Mehr Kashyap, Martin Seneviratne, Rae Woong Park, Sooyoung Yoo, Borim Ryu and Evan Minty and has published in prestigious journals such as Statistics in Medicine, Journal of the American Medical Informatics Association and BMC Medical Research Methodology.

In The Last Decade

Thomas Falconer

8 papers receiving 65 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Thomas Falconer United States 4 29 20 16 11 9 11 67
Alexander Davydov Belarus 3 26 0.9× 22 1.1× 13 0.8× 6 0.5× 19 2.1× 5 85
Vasileios Kaldis Greece 6 33 1.1× 15 0.8× 20 1.3× 10 0.9× 3 0.3× 14 107
Nandan Patibandla United States 4 9 0.3× 32 1.6× 17 1.1× 4 0.4× 7 0.8× 5 85
Elizabeth Zampino United States 4 18 0.6× 13 0.7× 10 0.6× 4 0.4× 10 1.1× 5 54
Dmitry Dymshyts United States 4 36 1.2× 20 1.0× 4 0.3× 8 0.7× 30 3.3× 4 95
Andrea Lorimer United Kingdom 3 6 0.2× 24 1.2× 10 0.6× 19 1.7× 14 1.6× 3 105
Andrew J. Steele United Kingdom 2 67 2.3× 49 2.5× 22 1.4× 7 0.6× 10 1.1× 2 157
Jason Ross United States 4 37 1.3× 25 1.3× 15 0.9× 3 0.3× 46 5.1× 7 141
Marie-Christine Fritzsche Germany 5 18 0.6× 9 0.5× 4 0.3× 4 0.4× 6 0.7× 6 90
Rajiv Nadukuru United States 5 21 0.7× 16 0.8× 10 0.6× 3 0.3× 19 2.1× 6 99

Countries citing papers authored by Thomas Falconer

Since Specialization
Citations

This map shows the geographic impact of Thomas Falconer'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 Thomas Falconer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Falconer more than expected).

Fields of papers citing papers by Thomas Falconer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Falconer. 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 Thomas Falconer. The network helps show where Thomas Falconer may publish in the future.

Co-authorship network of co-authors of Thomas Falconer

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Falconer. A scholar is included among the top collaborators of Thomas Falconer 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 Thomas Falconer. Thomas Falconer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Falconer, Thomas, Elizabeth Park, David Fiorentino, et al.. (2025). Identification of Adult Patients With Dermatomyositis Using Real‐World Data Sources. Arthritis Care & Research. 78(3). 362–370.
2.
Falconer, Thomas, et al.. (2025). Heterogeneity of treatment effects of glucose-lowering drug classes for type 2 diabetes: LEGEND-T2DM network real-world evidence. Journal of Diabetes and its Complications. 39(9). 109114–109114.
3.
4.
Oja, Marek, Kerli Mooses, Sulev Reisberg, et al.. (2024). Markov modeling for cost-effectiveness using federated health data network. Journal of the American Medical Informatics Association. 31(5). 1093–1101. 1 indexed citations
5.
Curtin, Catherine, Chen Yanover, Tal El‐Hay, et al.. (2024). Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network. Journal of the American Medical Informatics Association. 31(5). 1051–1061. 3 indexed citations
6.
Bu, Fan, Martijn J. Schuemie, Akihiko Nishimura, et al.. (2023). Bayesian safety surveillance with adaptive bias correction. Statistics in Medicine. 43(2). 395–418. 2 indexed citations
7.
Itzel, Timo, Thomas Falconer, Jimyung Park, et al.. (2023). Efficacy of Co-Medications in Patients with Alcoholic Liver Disease. Digestive Diseases. 41(5). 780–788. 3 indexed citations
8.
Li, Moying, Timo Itzel, Thomas Falconer, et al.. (2023). Impact of concomitant cardiovascular medications on overall survival in patients with liver cirrhosis. Scandinavian Journal of Gastroenterology. 58(12). 1505–1513. 1 indexed citations
9.
Kashyap, Mehr, Martin Seneviratne, Juan M. Banda, et al.. (2020). Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network. Journal of the American Medical Informatics Association. 27(6). 877–883. 20 indexed citations
10.
11.
Chen, Ruijun, Patrick Ryan, Karthik Natarajan, et al.. (2020). Treatment Patterns for Chronic Comorbid Conditions in Patients With Cancer Using a Large-Scale Observational Data Network. JCO Clinical Cancer Informatics. 4(4). 171–183. 15 indexed citations

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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