Tielman Van Vleck
- Molecular Biology
- Artificial Intelligence top 10%
- Epidemiology
- Health Information Management top 2%
- Nephrology top 5%
- Co-authors
- Girish N. NadkarniPeter D. StetsonStephen B. JohnsonLili ChanSteven G. CocaKumardeep ChaudharyNoémie ElhadadPattharawin Pattharanitima
- Topics
- Machine Learning in Healthcare (3 papers)Biomedical Text Mining and Ontologies (3 papers)Topic Modeling (3 papers)
- Partner nations
- United StatesUnited KingdomJapan
In The Last Decade
Tielman Van Vleck
15 papers receiving 480 citations
Peers
Comparison fields: 5 of 93
- Molecular Biology 136
- Artificial Intelligence 134
- Epidemiology 115
- Health Information Management 109
- Nephrology 98
Countries citing papers authored by Tielman Van Vleck
This map shows the geographic impact of Tielman Van Vleck'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 Tielman Van Vleck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tielman Van Vleck more than expected).
Fields of papers citing papers by Tielman Van Vleck
This network shows the impact of papers produced by Tielman Van Vleck. 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 Tielman Van Vleck. The network helps show where Tielman Van Vleck may publish in the future.
Co-authorship network of co-authors of Tielman Van Vleck
This figure shows the co-authorship network connecting the top 25 collaborators of Tielman Van Vleck. A scholar is included among the top collaborators of Tielman Van Vleck 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 Tielman Van Vleck. Tielman Van Vleck is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 10 | |
| 3 | 11 | |
| 4 | 19 | |
| 5 | 28 | |
| 6 | 15 | |
| 7 | 66 | |
| 8 | 43 | |
| 9 | 30 | |
| 10 | 43 | |
| 11 | 57 | |
| 12 | Corpus-Based Problem Selection for EHR Note Summarization. | 16 |
| 13 | Content and structure of clinical problem lists: a corpus analysis. | 26 |
| 14 | 109 | |
| 15 | Assessing data relevance for automated generation of a clinical summary. | 23 |
About Tielman Van Vleck
Tielman Van Vleck is a scholar working on Health Information Management, Issues, ethics and legal aspects and Nephrology, having authored 15 papers that have together received 497 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (3 papers), Biomedical Text Mining and Ontologies (3 papers) and Topic Modeling (3 papers). The work is most often cited by research in Health Information Management (109 citations), Health Informatics (28 citations) and Nephrology (98 citations). Tielman Van Vleck has collaborated with scholars based in United States, United Kingdom and Japan. Frequent co-authors include Girish N. Nadkarni, Peter D. Stetson, Stephen B. Johnson, Lili Chan, Steven G. Coca, Kumardeep Chaudhary, Noémie Elhadad, Pattharawin Pattharanitima, Kinsuk Chauhan and Áine Duffy. Their work appears in journals such as Kidney International, The American Journal of Human Genetics and Human Molecular Genetics.
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.