Serguei Pakhomov

5.2k citations
135 papers · 3.6k indexed · 1 hit paper · h-index 28
Topics
Biomedical Text Mining and Ontologies (62 papers)Topic Modeling (39 papers)Natural Language Processing Techniques (31 papers)
Journals
JAMASHILAP Revista de lepidopterologíaBioinformatics

In The Last Decade

Serguei Pakhomov

127 papers receiving 3.5k citations

Hit Papers

Systolic and Diastolic Heart Failure in the Community20062026201220192006200400600

Peers

Serguei Pakhomov
Comparison fields: 5 of 162
  • Artificial Intelligence 1.6k
  • Molecular Biology 1.4k
  • Cardiology and Cardiovascular Medicine 679
  • Health Information Management 357
  • Cognitive Neuroscience 266
Replace Vivian S. Gainer with:
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Serguei Pakhomov relative to Vivian S. Gainer United States Vivian S. Gainer's profile →
Citations per field
00.5×3.6×
Vivian S. Gainer · 1×
Citations per year

Countries citing papers authored by Serguei Pakhomov

Since Specialization
Citations

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

Fields of papers citing papers by Serguei Pakhomov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Serguei Pakhomov. 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 Serguei Pakhomov. The network helps show where Serguei Pakhomov may publish in the future.

Co-authorship network of co-authors of Serguei Pakhomov

This figure shows the co-authorship network connecting the top 25 collaborators of Serguei Pakhomov. A scholar is included among the top collaborators of Serguei Pakhomov 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 Serguei Pakhomov. Serguei Pakhomov 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 1
3 8
4 1
5 10
6 6
7 25
8 42
9
UMLS::Similarity: Measuring the Relatedness and Similarity of Biomedical Concepts
6
10 12
11
The role of the electronic medical record in the assessment of health related quality of life.
7
12
Using Second-order Vectors in a Knowledge-based Method for Acronym Disambiguation
11
13
Automated Identification of Synonyms in Biomedical Acronym Sense Inventories
6
14 53
15 17
16 381
17
Systolic and Diastolic Heart Failure in the Communitybreakdown →
666
18 37
19
Frame semantics and the domain of functioning, disability and health.
3
20 26

About Serguei Pakhomov

Serguei Pakhomov is a scholar working on Health Information Management, Artificial Intelligence and Issues, ethics and legal aspects, having authored 135 papers that have together received 3.6k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (62 papers), Topic Modeling (39 papers) and Natural Language Processing Techniques (31 papers). The work is most often cited by research in Health Information Management (357 citations), Artificial Intelligence (1.6k citations) and Health Informatics (65 citations). Serguei Pakhomov has collaborated with scholars based in United States, Switzerland and Canada. Frequent co-authors include Christopher G. Chute, Ted Pedersen, Genevieve B. Melton, Steven J. Jacobsen, Véronique L. Roger, Bridget T. McInnes, Siddharth Patwardhan, Francesca Bursi, Vuyisile T. Nkomo and Susan A. Weston. Their work appears in journals such as JAMA, SHILAP Revista de lepidopterología and Bioinformatics.

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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