Zubair Afzal

672 citations
27 papers · 392 · h-index 11

Impact in

Papers in

    • Topic Modeling 11
    • Natural Language Processing Techniques 6
    • Advanced Text Analysis Techniques 2
    • Machine Learning in Healthcare 2
    • Semantic Web and Ontologies 2
    • Biomedical Text Mining and Ontologies 13

Zubair Afzal

23 papers receiving 363 citations

Peers

Zubair Afzal
Comparison fields: 5 of 66
  • Health Information Management 28
  • Artificial Intelligence 199
  • Health Informatics 6
  • Toxicology 14
  • Molecular Biology 184
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Citations per year

Countries citing papers authored by Zubair Afzal

Since Specialization
Citations

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

Fields of papers citing papers by Zubair Afzal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Zubair Afzal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Zubair Afzal Line = papers co-authored together Zubair Afzal links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201282
2 201458
3 201339
4 201337
5 201433
6 201630
7 201228
8 202117
9 201617
10 201614
11
Erasmus MC at CLEF eHealth 2016: Concept recognition and coding in French texts
201610
12
Erasmus MC Approaches to the i2b2 Challenge
20097
13
Biomedical Concept Recognition in French Text Using Automatic Translation of English Terms.
20155
14 20243
15 20173
16
Automated identification of asthma patients within an electronical medical record database using machine learning
20122
17 20221
18 20221
19 20231
20 20221

About Zubair Afzal

Zubair Afzal is a scholar working on Artificial Intelligence, Molecular Biology, Information Systems, Information Systems and Management and Computational Theory and Mathematics, having authored 27 papers that have together received 392 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (13 papers), Topic Modeling (11 papers), Natural Language Processing Techniques (6 papers), Advanced Text Analysis Techniques (2 papers), Machine Learning in Healthcare (2 papers), Semantic Web and Ontologies (2 papers), Scientific Computing and Data Management (2 papers) and Computational Drug Discovery Methods (2 papers). The work is most often cited by research in Health Information Management (28 citations), Artificial Intelligence (199 citations), Health Informatics (6 citations), Toxicology (14 citations) and Molecular Biology (184 citations). Zubair Afzal has collaborated with scholars based in Netherlands, United States and Vietnam. Frequent co-authors include Jan A. Kors, Erik M. van Mulligen, Kang Ning, Bharat Singh, Martijn J. Schuemie, Miriam Sturkenboom, Ewoud Pons, Saber A. Akhondi, Katia Verhamme and Hettie M. Janssens. Their work appears in journals such as Database, BMC Bioinformatics, Pharmacoepidemiology and Drug Safety, Journal of the American Medical Informatics Association and Drug and Alcohol Dependence.

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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