William R. Hogan

7.3k citations
149 papers · 3.8k indexed · 1 hit paper · h-index 31
Topics
Biomedical Text Mining and Ontologies (50 papers)Electronic Health Records Systems (24 papers)Semantic Web and Ontologies (24 papers)
Journals
SHILAP Revista de lepidopterologíaBioinformaticsPLoS ONE

In The Last Decade

William R. Hogan

138 papers receiving 3.6k citations

Hit Papers

A large language model for electronic health records20222026202320242022100200300400

Peers

William R. Hogan
Comparison fields: 5 of 186
  • Artificial Intelligence 1.2k
  • Epidemiology 823
  • Molecular Biology 779
  • Health Information Management 556
  • Health Informatics 458
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William R. Hogan relative to Wendy W. Chapman United States Wendy W. Chapman's profile →
Citations per field
00.5×1.5×2.1×
Wendy W. Chapman · 1×
Citations per year

Countries citing papers authored by William R. Hogan

Since Specialization
Citations

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

Fields of papers citing papers by William R. Hogan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William R. Hogan. 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 William R. Hogan. The network helps show where William R. Hogan may publish in the future.

Co-authorship network of co-authors of William R. Hogan

This figure shows the co-authorship network connecting the top 25 collaborators of William R. Hogan. A scholar is included among the top collaborators of William R. Hogan 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 William R. Hogan. William R. Hogan 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
#WorkIndexed citations
1 0
2 0
3 12
4 8
5 0
6 3
7 1
8 17
9 186
10 31
11 14
12 15
13 28
14 12
15 52
16
A novel representation of terms related to infectious disease epidemiology for epidemic modeling The Apollo Structured Vocabulary and pre-existing representations
0
17
Simplifying MIREOT; a MIREOT Protege Plugin.
10
18
Representing the Reality Underlying Demographic Data.
4
19 56
20
Experience with Message Format and Code Set Standards for Early Warning Public Health Surveillance Systems
1

About William R. Hogan

William R. Hogan is a scholar working on Health Information Management, Health Informatics and Artificial Intelligence, having authored 149 papers that have together received 3.8k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (50 papers), Electronic Health Records Systems (24 papers) and Semantic Web and Ontologies (24 papers). The work is most often cited by research in Health Informatics (458 citations), Health Information Management (556 citations) and Artificial Intelligence (1.2k citations). William R. Hogan has collaborated with scholars based in United States, Norway and Germany. Frequent co-authors include Michael M. Wagner, Jiang Bian, Yonghui Wu, Xi Yang, Elizabeth Shenkman, Yi Guo, Gloria Lipori, Mathias Brochhausen, David L. Buckeridge and Howard Burkom. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

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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