Paul McNamee

10.4k citations
246 papers · 7.6k · 1 hit paper · h-index 48

Impact in

  • Nephrology top 1%
    • Dialysis and Renal Disease Management
    • Topic Modeling
    • Natural Language Processing Techniques
    • Semantic Web and Ontologies

Papers in

Paul McNamee

238 papers receiving 7.0k citations

Paul McNamee's Hit Papers

Critical appraisal tools 2014 · 324 citations
3240+4+8Years since publication100200300

Peers

Paul McNamee
Comparison fields: 5 of 196
  • Nephrology 468
  • Artificial Intelligence 1.7k
  • Ophthalmology 280
  • Transplantation 75
  • Psychiatry and Mental health 377
Replace Joshua C. Denny with:
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Paul McNamee relative to Joshua C. Denny United States Joshua C. Denny's profile →
Citations per field
00.5×2.8×
Joshua C. Denny · 1×
Citations per year

Countries citing papers authored by Paul McNamee

Since Specialization
Citations

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

Fields of papers citing papers by Paul McNamee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Paul McNamee, 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 Paul McNamee Line = papers co-authored together Paul McNamee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 246 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Critical appraisal tools
Hit paper breakdown →
2014324
2 2003247
3 2009246
4 2014239
5 2007238
6 2010234
7 1994198
8 1995194
9 2007187
10 2001182
11 2010166
12 1998166
13 2012123
14 2012120
15 2011108
16 201598
17 200389
18 200788
19 200286
20 201483

About Paul McNamee

Paul McNamee is a scholar working on Artificial Intelligence, General Health Professions, Economics and Econometrics, Information Systems and Management Science and Operations Research, having authored 246 papers that have together received 7.6k indexed citations. Recurring topics across this work include Topic Modeling (66 papers), Natural Language Processing Techniques (62 papers), Health Systems, Economic Evaluations, Quality of Life (21 papers), Semantic Web and Ontologies (18 papers), Data Quality and Management (17 papers), Information Retrieval and Search Behavior (16 papers), Psychological Well-being and Life Satisfaction (10 papers) and Health disparities and outcomes (10 papers). The work is most often cited by research in Nephrology (468 citations), Artificial Intelligence (1.7k citations), Ophthalmology (280 citations), Transplantation (75 citations) and Psychiatry and Mental health (377 citations). Paul McNamee has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include James Mayfield, Graham Scotland, Mark Dredze, Delip Rao, Ian Young, Silvia Coretti, Matteo Ruggeri, Christopher M. Loughrey, Christine Piatko and Gordon Prescott. Their work appears in journals such as Health Technology Assessment, Nephrology Dialysis Transplantation, Health Economics, Review of Economics of the Household and British Journal of Ophthalmology.

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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