Maria Kvist
About
In The Last Decade
Maria Kvist
33 papers receiving 564 citations
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 456
- Molecular Biology 353
- Health Information Management 83
- Toxicology 62
- Radiology, Nuclear Medicine and Imaging 33
Countries citing papers authored by Maria Kvist
This map shows the geographic impact of Maria Kvist'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 Maria Kvist with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maria Kvist more than expected).
Fields of papers citing papers by Maria Kvist
This network shows the impact of papers produced by Maria Kvist. 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 Maria Kvist. The network helps show where Maria Kvist may publish in the future.
Co-authorship network of co-authors of Maria Kvist
This figure shows the co-authorship network connecting the top 25 collaborators of Maria Kvist. A scholar is included among the top collaborators of Maria Kvist 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 Maria Kvist. Maria Kvist is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | Finding Cervical Cancer Symptoms in Swedish Clinical Text using a Machine Learning Approach and NegEx. | 17 |
| 3 | HEALTH BANK - A Workbench for Data Science Applications in Healthcare | 41 |
| 4 | 55 | |
| 5 | 75 | |
| 6 | 2 | |
| 7 | Handling Temporality of Clinical Events for Drug Safety Surveillance. | 27 |
| 8 | Detecting Healthcare-Associated Infections in Electronic Health Records : Evaluation of Machine Learning and Preprocessing Techniques | 7 |
| 9 | 80 | |
| 10 | 14 | |
| 11 | 13 | |
| 12 | Abbreviations in Swedish Clinical Text--use by three professions. | 8 |
| 13 | Corpus-Driven Terminology Development: Populating Swedish SNOMED CT with Synonyms Extracted from Electronic Health Records | 16 |
| 14 | 2 | |
| 15 | Negation Scope Delimitation in Clinical Text Using Three Approaches: NegEx, PyConTextNLP and SynNeg | 10 |
| 16 | Rule-based Entity Recognition and Coverage of SNOMED CT in Swedish Clinical Text | 26 |
| 17 | Exploration of Adverse Drug Reactions in Semantic Vector Space Models of Clinical Text | 10 |
| 18 | Entity Recognition of Pharmaceutical Drugs in Swedish Clinical Text | 3 |
| 19 | Initial Results in the Development of SCAN A Swedish Clinical Abbreviation Normalizer | 9 |
| 20 | Modeling human comprehension of Swedish medical records for intelligent access and summarization systems - Future vision, a physician's perspective | 10 |
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.