John P. Lalor

461 total citations
25 papers, 243 citations indexed

About

John P. Lalor is a scholar working on Artificial Intelligence, Information Systems and General Health Professions. According to data from OpenAlex, John P. Lalor has authored 25 papers receiving a total of 243 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 5 papers in Information Systems and 4 papers in General Health Professions. Recurrent topics in John P. Lalor's work include Topic Modeling (11 papers), Natural Language Processing Techniques (4 papers) and Health Literacy and Information Accessibility (4 papers). John P. Lalor is often cited by papers focused on Topic Modeling (11 papers), Natural Language Processing Techniques (4 papers) and Health Literacy and Information Accessibility (4 papers). John P. Lalor collaborates with scholars based in United States, Hong Kong and United Kingdom. John P. Lalor's co-authors include Hong Yu, Hao Wu, Tsendsuren Munkhdalai, Beverly Park Woolf, Pedro Rodríguez, Jordan Boyd‐Graber, Robin Jia, Joe Barrow, Jinying Chen and Weisong Liu and has published in prestigious journals such as MIS Quarterly, Journal of Medical Internet Research and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

John P. Lalor

22 papers receiving 238 citations

Peers

John P. Lalor
Comparison fields: 5 of 62
  • Artificial Intelligence 141
  • Information Systems 38
  • Health Information Management 27
  • General Health Professions 26
  • Molecular Biology 23
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Maja Hadzic Australia
Robin Aly Netherlands
Juan Antonio Lossio-Ventura United States
Mirac Süzgün United States
Serena Jeblee Canada
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Marta Rosecler Bez Brazil
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Matthew South United Kingdom View profile →
Citations per field, relative to John P. Lalor
John P. Lalor · 1×
Citations per year, relative to John P. Lalor
John P. Lalor · 1×

Countries citing papers authored by John P. Lalor

Since Specialization
Citations

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

Fields of papers citing papers by John P. Lalor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John P. Lalor. 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 John P. Lalor. The network helps show where John P. Lalor may publish in the future.

Co-authorship network of co-authors of John P. Lalor

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

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3 1
4 14
5 0
6 5
7 3
8 1
9 2
10 5
11 8
12 8
13 29
14 14
15 17
16 15
17 10
18
An Analysis of Machine Learning Intelligence.
2
19 37
20 15

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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