Kent A. Spackman

2.7k total citations
59 papers, 1.6k citations indexed

About

Kent A. Spackman is a scholar working on Molecular Biology, Artificial Intelligence and Language and Linguistics. According to data from OpenAlex, Kent A. Spackman has authored 59 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 41 papers in Artificial Intelligence and 14 papers in Language and Linguistics. Recurrent topics in Kent A. Spackman's work include Biomedical Text Mining and Ontologies (44 papers), Semantic Web and Ontologies (31 papers) and linguistics and terminology studies (14 papers). Kent A. Spackman is often cited by papers focused on Biomedical Text Mining and Ontologies (44 papers), Semantic Web and Ontologies (31 papers) and linguistics and terminology studies (14 papers). Kent A. Spackman collaborates with scholars based in United States, Austria and United Kingdom. Kent A. Spackman's co-authors include Keith E. Campbell, Roger A. Côté, Colin Price, Angela Yee‐Moon Wang, Yehoshua Perl, Michael Halper, Stefan Schulz, Amy Wang, Hua Min and William Hersh and has published in prestigious journals such as BMC Bioinformatics, Journal of the American Medical Informatics Association and Computer Methods and Programs in Biomedicine.

In The Last Decade

Kent A. Spackman

58 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kent A. Spackman United States 22 1.2k 1.1k 287 223 116 59 1.6k
Stefan Schulz Germany 22 1.5k 1.2× 1.5k 1.3× 346 1.2× 154 0.7× 156 1.3× 187 2.3k
D. A. B. Lindberg United States 15 1.5k 1.2× 1.3k 1.2× 415 1.4× 98 0.4× 151 1.3× 45 2.2k
Keith E. Campbell United States 17 962 0.8× 745 0.7× 380 1.3× 161 0.7× 114 1.0× 39 1.3k
Pierre Zweigenbaum France 24 1.4k 1.1× 2.0k 1.8× 159 0.6× 166 0.7× 48 0.4× 165 2.5k
Robert Baud Switzerland 19 681 0.5× 764 0.7× 212 0.7× 122 0.5× 49 0.4× 89 1.1k
Patrick Ruch Switzerland 22 1.3k 1.0× 1.2k 1.1× 137 0.5× 31 0.1× 37 0.3× 150 1.8k
W. A. Nowlan United Kingdom 12 588 0.5× 581 0.5× 276 1.0× 81 0.4× 56 0.5× 19 861
Diane E. Oliver United States 14 684 0.6× 416 0.4× 284 1.0× 62 0.3× 185 1.6× 30 1.1k
Aurélie Névéol France 19 809 0.7× 944 0.9× 79 0.3× 32 0.1× 50 0.4× 71 1.4k
Sherri de Coronado United States 12 752 0.6× 562 0.5× 47 0.2× 73 0.3× 37 0.3× 23 926

Countries citing papers authored by Kent A. Spackman

Since Specialization
Citations

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

Fields of papers citing papers by Kent A. Spackman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kent A. Spackman. 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 Kent A. Spackman. The network helps show where Kent A. Spackman may publish in the future.

Co-authorship network of co-authors of Kent A. Spackman

This figure shows the co-authorship network connecting the top 25 collaborators of Kent A. Spackman. A scholar is included among the top collaborators of Kent A. Spackman 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 Kent A. Spackman. Kent A. Spackman 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.
Wang, Yue, Michael Halper, Huanying Gu, et al.. (2011). Auditing complex concepts of SNOMED using a refined hierarchical abstraction network. Journal of Biomedical Informatics. 45(1). 1–14. 45 indexed citations
2.
Schulz, Stefan, et al.. (2011). Scalable representations of diseases in biomedical ontologies. Journal of Biomedical Semantics. 2(S2). S6–S6. 26 indexed citations
3.
Jiang, Guoqian, Harold R. Solbrig, R.J.G. Chalmers, et al.. (2011). A case study of ICD-11 anatomy value set extraction from SNOMED CT. 833. 133–138. 2 indexed citations
4.
Cornet, Ronald & Kent A. Spackman. (2008). Representing and sharing knowledge using SNOMED. Pure Amsterdam UMC. 410. 2 indexed citations
5.
Spackman, Kent A.. (2007). An Examination of OWL and the Requirements of a Large Health Care Terminology.. 6 indexed citations
6.
Wang, Yue, Michael Halper, Hua Min, et al.. (2006). Structural methodologies for auditing SNOMED. Journal of Biomedical Informatics. 40(5). 561–581. 83 indexed citations
7.
Zimmerman, Kathy, Jeff R. Wilcke, John L. Robertson, et al.. (2005). SNOMED representation of explanatory knowledge in veterinary clinical pathology. Veterinary Clinical Pathology. 34(1). 7–16. 6 indexed citations
8.
Spackman, Kent A.. (2004). SNOMED CT milestones: endorsements are added to already-impressive standards credentials.. PubMed. 21(9). 54, 56–54, 56. 45 indexed citations
9.
Masarie, Fred E., et al.. (2003). The use of SNOMED CT simplifies querying of a clinical data warehouse.. PubMed. 910–910. 20 indexed citations
10.
Wang, Amy & Kent A. Spackman. (2002). The Grouping of Roles in SNOMED Clinical Terms.. American Medical Informatics Association Annual Symposium. 125(3). 1192–1192. 1 indexed citations
11.
Bidgood, W. Dean, Bruce E. Bray, Nicholas Brown, et al.. (1999). Image Acquisition Context: Procedure Description Attributes for Clinically Relevant Indexing and Selective Retrieval of Biomedical Images. Journal of the American Medical Informatics Association. 6(1). 61–75. 21 indexed citations
12.
Dolin, Robert H., Stanley M. Huff, R.A. Rocha, Kent A. Spackman, & Keith E. Campbell. (1998). Evaluation of a "Lexically Assign, Logically Refine" Strategy for Semi-automated Integration of Overlapping Terminologies. Journal of the American Medical Informatics Association. 5(2). 203–213. 27 indexed citations
13.
Levy, Donald H., Robert H. Dolin, John Mattison, Kent A. Spackman, & Keith E. Campbell. (1998). Computer-facilitated collaboration: experiences building SNOMED-RT.. PubMed. 870–4. 6 indexed citations
14.
Bidgood, W. Dean, Louis Y. Korman, Alan M. Golichowski, et al.. (1997). Medical data standards. International Journal on Digital Libraries. 1(3). 7 indexed citations
15.
Spackman, Kent A., et al.. (1996). An Expert System to Diagnose Anemia and Report Results Directly on Hematology Forms. Computers and Biomedical Research. 29(1). 16–26. 29 indexed citations
16.
Spackman, Kent A., et al.. (1989). Evaluation of Neural Network Performance by Receiver Operating Characteristic Analysis: Examples from the Biotechnology Domain. PubMed Central. 295–301. 7 indexed citations
17.
Spackman, Kent A., et al.. (1988). The Transfusion Advisor: A Knowledge-Based System for the Blood Bank. PubMed Central. 18–21. 4 indexed citations
18.
Spackman, Kent A.. (1988). Creating decision criteria from examples: the criteria learning system (CRLS). 1 indexed citations
19.
Spackman, Kent A.. (1985). A program for machine learning of counting criteria: empirical induction of logic-based classification rules. Computer Methods and Programs in Biomedicine. 21(3). 221–226. 1 indexed citations
20.
Michalski, Ryszard S., et al.. (1983). A logic-based approach to conceptual data base analysis. Medical Informatics. 8(3). 187–195. 5 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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