Meliha Yetişgen

1.3k citations
53 papers · 641 indexed · h-index 13
Topics
Topic Modeling (27 papers)Biomedical Text Mining and Ontologies (22 papers)Machine Learning in Healthcare (17 papers)

In The Last Decade

Meliha Yetişgen

48 papers receiving 631 citations

Peers

Meliha Yetişgen
Comparison fields: 5 of 90
  • Artificial Intelligence 348
  • Molecular Biology 206
  • Health Information Management 119
  • Radiology, Nuclear Medicine and Imaging 108
  • Health Informatics 95
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Citations per field
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Citations per year

Countries citing papers authored by Meliha Yetişgen

Since Specialization
Citations

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

Fields of papers citing papers by Meliha Yetişgen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meliha Yetişgen

This figure shows the co-authorship network connecting the top 25 collaborators of Meliha Yetişgen. A scholar is included among the top collaborators of Meliha Yetişgen 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 Meliha Yetişgen. Meliha Yetişgen 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 2
2 1
3 2
4 1
5 0
6 20
7 0
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13
Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge Distillation.
6
14
Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation
2
15 13
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Annotating and Detecting Medical Events in Clinical Notes
1
17 17
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Biomedical/Clinical NLP
2
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Annotating Clinical Events in Text Snippets for Phenotype Detection
1
20 7

About Meliha Yetişgen

Meliha Yetişgen is a scholar working on Health Informatics, Health Information Management and Issues, ethics and legal aspects, having authored 53 papers that have together received 641 indexed citations. Recurring topics across this work include Topic Modeling (27 papers), Biomedical Text Mining and Ontologies (22 papers) and Machine Learning in Healthcare (17 papers). The work is most often cited by research in Health Informatics (95 citations), Health Information Management (119 citations) and Issues, ethics and legal aspects (15 citations). Meliha Yetişgen has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Wen-wai Yim, Sharon W. Kwan, William Proctor Harris, Kevin Lybarger, Mari Ostendorf, Özlem Uzuner, Peter Tarczy‐Hornoch, Daniel Capurro, Erik G. Van Eaton and Asma Ben Abacha. Their work appears in journals such as SHILAP Revista de lepidopterología, Cancer Discovery and Journal of the American Medical Informatics Association.

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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