Thomas Searle

19 papers receiving 345 citations

Peers

Thomas Searle
Comparison fields: 5 of 69
  • Health Informatics 24
  • Infectious Diseases 126
  • Reproductive Medicine 37
  • Health Information Management 13
  • Neurology 38
Replace Antonella Rispoli with:
Antonella Rispoli Italy
Alessandra De Palma Italy
Walaa Alsharif Saudi Arabia
Nidhi Naik United States
Anthony Shek United Kingdom
Sandy Joung United States
Deb Sanjay Nag India
Ruobing Lei China
Daniel Laxar Austria
Johan N Siebert Switzerland
Thomas Searle relative to Antonella Rispoli Italy Antonella Rispoli's profile →
Citations per field
00.5×12.3×
Antonella Rispoli · 1×
Citations per year

Countries citing papers authored by Thomas Searle

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Searle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Thomas Searle, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Thomas Searle Line = papers co-authored together Thomas Searle links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2020152
2 201344
3 202335
4 202322
5 200315
6 202215
7 200212
8 200310
9 20219
10 20248
11 20217
12 20245
13 20243
14 20213
15 20023
16 20252
17 20241
18 20021
19 20201
20 20251

About Thomas Searle

Thomas Searle is a scholar working on Public Health, Environmental and Occupational Health, Reproductive Medicine, Infectious Diseases, Molecular Biology and Artificial Intelligence, having authored 20 papers that have together received 349 indexed citations. Recurring topics across this work include Ovarian function and disorders (3 papers), Machine Learning in Healthcare (3 papers), COVID-19 Clinical Research Studies (3 papers), Biomedical Text Mining and Ontologies (3 papers), Topic Modeling (2 papers), Heart Failure Treatment and Management (2 papers), Reproductive Biology and Fertility (2 papers) and Assisted Reproductive Technology and Twin Pregnancy (2 papers). The work is most often cited by research in Health Informatics (24 citations), Infectious Diseases (126 citations), Reproductive Medicine (37 citations), Health Information Management (13 citations) and Neurology (38 citations). Thomas Searle has collaborated with scholars based in United Kingdom, Norway and Italy. Frequent co-authors include Richard Dobson, James Teo, Daniel Bean, Kevin O’Gallagher, Željko Kraljević, Ajay M. Shah, Rebecca Bendayan, Anthony Shek, Amos Folarin and Andrew Pickles. Their work appears in journals such as Acta Obstetricia Et Gynecologica Scandinavica, European Journal of Heart Failure, Journal of the American Medical Informatics Association, Journal of Biomedical Informatics and European Psychiatry.

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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