Monica Agrawal
About
In The Last Decade
Monica Agrawal
17 papers receiving 933 citations
Hit Papers
Peers
Comparison fields: 5 of 125
- Molecular Biology 447
- Artificial Intelligence 386
- Computational Theory and Mathematics 381
- Materials Chemistry 94
- Computer Vision and Pattern Recognition 65
Countries citing papers authored by Monica Agrawal
This map shows the geographic impact of Monica Agrawal'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 Monica Agrawal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Monica Agrawal more than expected).
Fields of papers citing papers by Monica Agrawal
This network shows the impact of papers produced by Monica Agrawal. 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 Monica Agrawal. The network helps show where Monica Agrawal may publish in the future.
Co-authorship network of co-authors of Monica Agrawal
This figure shows the co-authorship network connecting the top 25 collaborators of Monica Agrawal. A scholar is included among the top collaborators of Monica Agrawal 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 Monica Agrawal. Monica Agrawal 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 | 1 | |
| 3 | Large language models are few-shot clinical information extractors breakdown → | 142 |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 10 | |
| 7 | 1 | |
| 8 | Fast, Structured Clinical Documentation via Contextual Autocomplete. | 2 |
| 9 | 2 | |
| 10 | 8 | |
| 11 | 1 | |
| 12 | 2 | |
| 13 | 2 | |
| 14 | 0 | |
| 15 | 1 | |
| 16 | 1 | |
| 17 | Modeling polypharmacy side effects with graph convolutional networks breakdown → | 774 |
| 18 | 0 | |
| 19 | 8 | |
| 20 | 1 |
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.