Lena Wiese
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Health Information Management top 10%
- Co-authors
- Joachim BiskupStefanie ScherzingerChristin SeifertMarcel H. SchulzMichael BrennerKarsten KrügerKaren ZentgrafMichael Mutz
- Topics
- Machine Learning in Healthcare (8 papers)Distributed systems and fault tolerance (5 papers)Advanced Data Storage Technologies (4 papers)
- Partner nations
- GermanySaudi ArabiaNetherlands
In The Last Decade
Lena Wiese
32 papers receiving 161 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 84
- Computer Networks and Communications 57
- Information Systems 54
- Computer Vision and Pattern Recognition 18
- Health Information Management 17
Countries citing papers authored by Lena Wiese
This map shows the geographic impact of Lena Wiese'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 Lena Wiese with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lena Wiese more than expected).
Fields of papers citing papers by Lena Wiese
This network shows the impact of papers produced by Lena Wiese. 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 Lena Wiese. The network helps show where Lena Wiese may publish in the future.
Co-authorship network of co-authors of Lena Wiese
This figure shows the co-authorship network connecting the top 25 collaborators of Lena Wiese. A scholar is included among the top collaborators of Lena Wiese 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 Lena Wiese. Lena Wiese 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 | 1 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 0 | |
| 9 | 0 | |
| 10 | 0 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | Automating Bronchoconstriction Analysis based on U-Net. | 0 |
| 14 | 7 | |
| 15 | 2 | |
| 16 | 6 | |
| 17 | Comparative Evaluation for Recommender Systems for Book Recommendations | 4 |
| 18 | Polyglot Database Architectures = Polyglot Challenges. | 7 |
| 19 | 1 | |
| 20 | 6 |
About Lena Wiese
Lena Wiese is a scholar working on Health Information Management, Health Informatics and Artificial Intelligence, having authored 40 papers that have together received 166 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (8 papers), Distributed systems and fault tolerance (5 papers) and Advanced Data Storage Technologies (4 papers). The work is most often cited by research in Health Informatics (5 citations), Health Information Management (17 citations) and Artificial Intelligence (84 citations). Lena Wiese has collaborated with scholars based in Germany, Saudi Arabia and Netherlands. Frequent co-authors include Joachim Biskup, Stefanie Scherzinger, Christin Seifert, Marcel H. Schulz, Michael Brenner, Karsten Krüger, Karen Zentgraf, Michael Mutz, Markus Raab and Marcus Baum. Their work appears in journals such as Journal of Clinical Epidemiology, BMC Bioinformatics and Theoretical Computer Science.
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.