Laura Cabello
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
- General Social Sciences top 5%
- Computational and Text Analysis Methods
Papers in
-
- Advanced Text Analysis Techniques 1
- Sentiment Analysis and Opinion Mining 1
-
- Artificial Intelligence in Healthcare and Education 2
- Co-authors
- Daniel Hershcovich (2 shared papers)Yong Cao (2 shared papers)Seolhwa Lee (1 shared paper)Li Zhou (1 shared paper)Min Chen (1 shared paper)Anders Søgaard (1 shared paper)Eulogio Valentı́n (1 shared paper)Piet W. J. de Groot (1 shared paper)
- Journals
- FEMS Yeast Research (1 paper)Procesamiento del lenguaje natural (1 paper)SSRN Electronic Journal (1 paper)Research at the University of Copenhagen (University of Copenhagen) (3 papers)
- Partner nations
- DenmarkChinaSouth Korea
In The Last Decade
Laura Cabello
6 papers receiving 78 citations
Peers
Comparison fields: 5 of 49
- Health Informatics 12
- General Social Sciences 11
- Artificial Intelligence 47
- Safety Research 6
- Periodontics 3
Countries citing papers authored by Laura Cabello
This map shows the geographic impact of Laura Cabello'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 Laura Cabello with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laura Cabello more than expected).
Fields of papers citing papers by Laura Cabello
This network shows the impact of papers produced by Laura Cabello. 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 Laura Cabello. The network helps show where Laura Cabello may publish in the future.
Co-authors
The 16 scholars most cited alongside Laura Cabello, 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 | 2023 | 61 | |
| 2 | 2018 | 10 | |
| 3 | 2023 | 4 | |
| 4 | 2023 | 3 | |
| 5 | 2018 | 1 | |
| 6 | 2023 | 1 | |
| 7 | 2024 | 0 |
About Laura Cabello
Laura Cabello is a scholar working on Artificial Intelligence, Health Informatics, Infectious Diseases, Occupational Therapy and Social Psychology, having authored 7 papers that have together received 80 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (2 papers), Advanced Text Analysis Techniques (1 paper), Bullying, Victimization, and Aggression (1 paper), FinTech, Crowdfunding, Digital Finance (1 paper), Digital Accessibility for Disabilities (1 paper), Ethics and Social Impacts of AI (1 paper), Antifungal resistance and susceptibility (1 paper) and Sentiment Analysis and Opinion Mining (1 paper). The work is most often cited by research in Health Informatics (12 citations), General Social Sciences (11 citations), Artificial Intelligence (47 citations), Safety Research (6 citations) and Periodontics (3 citations). Laura Cabello has collaborated with scholars based in Denmark, China and South Korea. Frequent co-authors include Daniel Hershcovich, Yong Cao, Seolhwa Lee, Li Zhou, Min Chen, Anders Søgaard, Eulogio Valentı́n, Piet W. J. de Groot, María Martínez‐Esparza and Sergi Maicas. Their work appears in journals such as FEMS Yeast Research, Procesamiento del lenguaje natural, SSRN Electronic Journal and Research at the University of Copenhagen (University of Copenhagen).
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.