Dan Lahav
Impact in
- Health Informatics top 10%
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- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning in Healthcare
Papers in
-
- Topic Modeling 6
- Natural Language Processing Techniques 3
- Domain Adaptation and Few-Shot Learning 1
- Advanced Text Analysis Techniques 1
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- Misinformation and Its Impacts 3
- Co-authors
- Noam Slonim (5 shared papers)Shai Gretz (4 shared papers)Assaf Toledo (4 shared papers)Noam Shomron (2 shared papers)Yazeed Zoabi (1 shared paper)Amos Adler (1 shared paper)Ahuva Weiss‐Meilik (1 shared paper)U. Orgad (1 shared paper)
- Journals
- Scientific Reports (1 paper)Vector-Borne and Zoonotic Diseases (1 paper)The Veterinary Journal (1 paper)Journal of Medical Internet Research (1 paper)JMIR Human Factors (1 paper)
- Partner nations
- IsraelUnited StatesUnited Kingdom
In The Last Decade
Dan Lahav
11 papers receiving 160 citations
Peers
Comparison fields: 5 of 73
- Health Informatics 13
- Artificial Intelligence 82
- Applied Psychology 11
- Applied Microbiology and Biotechnology 4
- Health 11
Countries citing papers authored by Dan Lahav
This map shows the geographic impact of Dan Lahav'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 Dan Lahav with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Lahav more than expected).
Fields of papers citing papers by Dan Lahav
This network shows the impact of papers produced by Dan Lahav. 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 Dan Lahav. The network helps show where Dan Lahav may publish in the future.
Co-authors
The 25 scholars most cited alongside Dan Lahav, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 25 | |
| 2 | 2021 | 22 | |
| 3 | 2006 | 19 | |
| 4 | 2007 | 19 | |
| 5 | 2022 | 18 | |
| 6 | 2022 | 17 | |
| 7 | 2022 | 15 | |
| 8 | 2019 | 13 | |
| 9 | 2020 | 12 | |
| 10 | 2021 | 5 | |
| 11 | 2023 | 1 | |
| 12 | 2023 | 0 |
About Dan Lahav
Dan Lahav is a scholar working on Artificial Intelligence, Sociology and Political Science, Molecular Biology, Health and Applied Psychology, having authored 12 papers that have together received 166 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (3 papers), Misinformation and Its Impacts (3 papers), Biomedical Text Mining and Ontologies (2 papers), Digital Mental Health Interventions (2 papers), Vaccine Coverage and Hesitancy (2 papers), Domain Adaptation and Few-Shot Learning (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Health Informatics (13 citations), Artificial Intelligence (82 citations), Applied Psychology (11 citations), Applied Microbiology and Biotechnology (4 citations) and Health (11 citations). Dan Lahav has collaborated with scholars based in Israel, United States and United Kingdom. Frequent co-authors include Noam Slonim, Shai Gretz, Assaf Toledo, Noam Shomron, Yazeed Zoabi, Amos Adler, Ahuva Weiss‐Meilik, U. Orgad, Naor Bar‐Zeev and Eran Lavy. Their work appears in journals such as Scientific Reports, Vector-Borne and Zoonotic Diseases, The Veterinary Journal, Journal of Medical Internet Research and JMIR Human Factors.
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.