John P. Lalor
- Artificial Intelligence top 10%
- Information Systems
- Health Information Management top 10%
- General Health Professions
- Molecular Biology
- Co-authors
- Hong YuHao WuTsendsuren MunkhdalaiBeverly Park WoolfPedro RodríguezJordan Boyd‐GraberRobin JiaJoe Barrow
- Topics
- Topic Modeling (11 papers)Natural Language Processing Techniques (4 papers)Health Literacy and Information Accessibility (4 papers)
- Journals
- MIS QuarterlyJournal of Medical Internet ResearchIEEE Transactions on Knowledge and Data Engineering
- Partner nations
- United StatesHong KongUnited Kingdom
In The Last Decade
John P. Lalor
22 papers receiving 238 citations
Peers
Comparison fields: 5 of 62
- Artificial Intelligence 141
- Information Systems 38
- Health Information Management 27
- General Health Professions 26
- Molecular Biology 23
Countries citing papers authored by John P. Lalor
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
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
| # | 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 |
About John P. Lalor
John P. Lalor is a scholar working on Health Information Management, Artificial Intelligence and Safety Research, having authored 25 papers that have together received 243 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Natural Language Processing Techniques (4 papers) and Health Literacy and Information Accessibility (4 papers). The work is most often cited by research in Health Information Management (27 citations), Artificial Intelligence (141 citations) and Health Informatics (5 citations). John P. Lalor has collaborated with scholars based in United States, Hong Kong and United Kingdom. Frequent 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. Their work appears in journals such as MIS Quarterly, Journal of Medical Internet Research and IEEE Transactions on Knowledge and Data Engineering.
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.