Daniel J. Vreeman

1.1k total citations
47 papers, 684 citations indexed

About

Daniel J. Vreeman is a scholar working on Molecular Biology, Health Information Management and Artificial Intelligence. According to data from OpenAlex, Daniel J. Vreeman has authored 47 papers receiving a total of 684 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 19 papers in Health Information Management and 12 papers in Artificial Intelligence. Recurrent topics in Daniel J. Vreeman's work include Biomedical Text Mining and Ontologies (31 papers), Electronic Health Records Systems (19 papers) and Data Quality and Management (11 papers). Daniel J. Vreeman is often cited by papers focused on Biomedical Text Mining and Ontologies (31 papers), Electronic Health Records Systems (19 papers) and Data Quality and Management (11 papers). Daniel J. Vreeman collaborates with scholars based in United States, Australia and Sweden. Daniel J. Vreeman's co-authors include Clement J. McDonald, Stanley M. Huff, O Bodenreider, Ronald Cornet, Brian E. Dixon, Shaun J. Grannis, Ming‐Chin Lin, Christopher Robbins, Stephen Wilson and M. S. Sothmann and has published in prestigious journals such as The Journal of Pediatrics, Physical Therapy and Radiographics.

In The Last Decade

Daniel J. Vreeman

44 papers receiving 655 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel J. Vreeman United States 17 305 274 172 105 102 47 684
Todd Ferris United States 9 175 0.6× 141 0.5× 178 1.0× 71 0.7× 97 1.0× 11 854
M.S. Mendis Sri Lanka 6 223 0.7× 201 0.7× 240 1.4× 98 0.9× 98 1.0× 14 728
Georges De Moor Belgium 15 248 0.8× 309 1.1× 279 1.6× 82 0.8× 208 2.0× 51 927
Andrew Wen United States 18 278 0.9× 199 0.7× 373 2.2× 52 0.5× 80 0.8× 64 875
Rebecca Kush United States 14 205 0.7× 204 0.7× 136 0.8× 81 0.8× 174 1.7× 30 693
David Kreda United States 9 180 0.6× 412 1.5× 149 0.9× 68 0.6× 185 1.8× 21 868
Brecht Claerhout Spain 10 188 0.6× 218 0.8× 193 1.1× 67 0.6× 168 1.6× 31 827
Éric Zapletal France 14 142 0.5× 163 0.6× 144 0.8× 55 0.5× 77 0.8× 34 639
Hans Åhlfeldt Sweden 15 212 0.7× 198 0.7× 162 0.9× 51 0.5× 83 0.8× 64 976
Sunyang Fu United States 18 259 0.8× 139 0.5× 521 3.0× 42 0.4× 73 0.7× 86 1.1k

Countries citing papers authored by Daniel J. Vreeman

Since Specialization
Citations

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

Fields of papers citing papers by Daniel J. Vreeman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel J. Vreeman. 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 Daniel J. Vreeman. The network helps show where Daniel J. Vreeman may publish in the future.

Co-authorship network of co-authors of Daniel J. Vreeman

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel J. Vreeman. A scholar is included among the top collaborators of Daniel J. Vreeman 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 Daniel J. Vreeman. Daniel J. Vreeman 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
1.
Jaffe, Charles L., et al.. (2023). Implementing HL7 FHIR. 2(1). 1–8. 1 indexed citations
2.
Cullen, Theresa, et al.. (2017). The Capture of Social and Behavioral Determinants of Health in Electronic Health Records.. AMIA. 1 indexed citations
3.
Campbell, W. Scott, Daniel Karlsson, Daniel J. Vreeman, et al.. (2017). A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC. PMC. 1 indexed citations
4.
Jones, Josette, et al.. (2017). Reconciling disparate information in continuity of care documents: Piloting a system to consolidate structured clinical documents. Journal of Biomedical Informatics. 74. 123–129. 8 indexed citations
5.
Vreeman, Daniel J., et al.. (2017). Standard Anatomic Terminologies: Comparison for Use in a Health Information Exchange–Based Prior Computed Tomography (CT) Alerting System. JMIR Medical Informatics. 5(4). e49–e49. 2 indexed citations
6.
Vreeman, Daniel J., et al.. (2015). Possibilities and implications of using the ICF and other vocabulary standards in electronic health records. PMC. 1 indexed citations
7.
Dixon, Brian E., J. N. Hook, & Daniel J. Vreeman. (2015). Learning From the Crowd in Terminology Mapping: The LOINC Experience. Laboratory Medicine. 46(2). 168–174. 7 indexed citations
8.
Vreeman, Daniel J., et al.. (2014). A corpus-based approach for automated LOINC mapping. PMC. 1 indexed citations
9.
Dixon, Brian E., Daniel J. Vreeman, & Shaun J. Grannis. (2014). The long road to semantic interoperability in support of public health: Experiences from two states. Journal of Biomedical Informatics. 49. 3–8. 43 indexed citations
10.
Vreeman, Daniel J., et al.. (2013). Possibilities and Implications of Using the ICF and Other Vocabulary Standards in Electronic Health Records. Physiotherapy Research International. 20(4). 210–219. 19 indexed citations
11.
Vreeman, Daniel J., et al.. (2013). A corpus-based approach for automated LOINC mapping. Journal of the American Medical Informatics Association. 21(1). 64–72. 13 indexed citations
12.
Vreeman, Daniel J., et al.. (2012). Enabling international adoption of LOINC through translation. Journal of Biomedical Informatics. 45(4). 667–673. 26 indexed citations
13.
Lin, Ming‐Chin, Daniel J. Vreeman, Clement J. McDonald, & Stanley M. Huff. (2012). Auditing consistency and usefulness of LOINC use among three large institutions – Using version spaces for grouping LOINC codes. Journal of Biomedical Informatics. 45(4). 658–666. 17 indexed citations
14.
Pan, Huaqin, K. A. Tryka, Daniel J. Vreeman, et al.. (2012). Using PhenX measures to identify opportunities for cross-study analysis. Human Mutation. 33(5). 849–857. 24 indexed citations
15.
Kroth, Philip J, et al.. (2011). Using LOINC to link 10 terminology standards to one unified standard in a specialized domain. Journal of Biomedical Informatics. 45(4). 674–682. 4 indexed citations
16.
Lin, Ming‐Chin, Daniel J. Vreeman, Clement J. McDonald, & Stanley M. Huff. (2010). A Characterization of Local LOINC Mapping for Laboratory Tests in Three Large Institutions. Methods of Information in Medicine. 50(2). 105–114. 30 indexed citations
17.
Vreeman, Daniel J., Clement J. McDonald, & Stanley M. Huff. (2010). LOINC®: a universal catalogue of individual clinical observations and uniform representation of enumerated collections. PubMed. 3(4). 273–273. 34 indexed citations
18.
Robbins, Christopher, Daniel J. Vreeman, M. S. Sothmann, Stephen Wilson, & Neil Oldridge. (2009). A Review of the Long-Term Health Outcomes Associated With War-Related Amputation. Military Medicine. 174(6). 588–592. 73 indexed citations
19.
Vreeman, Daniel J., et al.. (2008). Embracing change in a health information exchange.. PubMed. 768–72. 18 indexed citations
20.
Vreeman, Rachel, Kristine A. Madsen, Daniel J. Vreeman, Aaron E. Carroll, & Stephen M. Downs. (2006). Compliance with guidelines for ADHD: A pilot study of an evaluation tool. The Journal of Pediatrics. 149(4). 568–571.e1. 4 indexed citations

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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