Swapna Abhyankar
- Artificial Intelligence top 10%
- Molecular Biology
- Health Information Management top 5%
- Epidemiology
- Pulmonary and Respiratory Medicine
- Co-authors
- Dina Demner‐FushmanClement J. McDonaldFiona CallaghanKira LeishearHalil KilicogluKirk RobertsSonya E. ShooshanLaritza Rodriguez
- Topics
- Biomedical Text Mining and Ontologies (7 papers)Machine Learning in Healthcare (4 papers)Topic Modeling (3 papers)
- Partner nations
- United StatesSpainFrance
In The Last Decade
Swapna Abhyankar
17 papers receiving 303 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 107
- Molecular Biology 104
- Health Information Management 60
- Epidemiology 43
- Pulmonary and Respiratory Medicine 39
Countries citing papers authored by Swapna Abhyankar
This map shows the geographic impact of Swapna Abhyankar'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 Swapna Abhyankar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Swapna Abhyankar more than expected).
Fields of papers citing papers by Swapna Abhyankar
This network shows the impact of papers produced by Swapna Abhyankar. 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 Swapna Abhyankar. The network helps show where Swapna Abhyankar may publish in the future.
Co-authorship network of co-authors of Swapna Abhyankar
This figure shows the co-authorship network connecting the top 25 collaborators of Swapna Abhyankar. A scholar is included among the top collaborators of Swapna Abhyankar 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 Swapna Abhyankar. Swapna Abhyankar 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 | 6 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 21 | |
| 6 | 4 | |
| 7 | 29 | |
| 8 | 13 | |
| 9 | 49 | |
| 10 | 19 | |
| 11 | A simple method to extract key maternal data from neonatal clinical notes. | 6 |
| 12 | NLM at TREC 2012 Medical Records Track | 5 |
| 13 | A Critical Window of Opportunity to Standardize Genetic Testing Results. | 1 |
| 14 | 34 | |
| 15 | 84 | |
| 16 | A Knowledge-Based Approach to Medical Records Retrieval. | 25 |
| 17 | Newborn Screening Health Information Exchange: Updated Guidance for Coding and HL7 Electronic Messaging | 1 |
| 18 | Standardizing newborn screening results for health information exchange. | 15 |
About Swapna Abhyankar
Swapna Abhyankar is a scholar working on Health Information Management, Issues, ethics and legal aspects and Toxicology, having authored 18 papers that have together received 315 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (7 papers), Machine Learning in Healthcare (4 papers) and Topic Modeling (3 papers). The work is most often cited by research in Health Information Management (60 citations), Health Informatics (9 citations) and Issues, ethics and legal aspects (6 citations). Swapna Abhyankar has collaborated with scholars based in United States, Spain and France. Frequent co-authors include Dina Demner‐Fushman, Clement J. McDonald, Fiona Callaghan, Kira Leishear, Halil Kilicoglu, Kirk Roberts, Sonya E. Shooshan, Laritza Rodriguez, Antonio Jimeno Yepes and François-Michel Lang. Their work appears in journals such as Critical Care, Clinica Chimica Acta and Radiographics.
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.