Surabhi Datta

946 total citations · 1 hit paper
19 papers, 483 citations indexed

About

Surabhi Datta is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Surabhi Datta has authored 19 papers receiving a total of 483 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 9 papers in Artificial Intelligence and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Surabhi Datta's work include Biomedical Text Mining and Ontologies (10 papers), Topic Modeling (7 papers) and Natural Language Processing Techniques (6 papers). Surabhi Datta is often cited by papers focused on Biomedical Text Mining and Ontologies (10 papers), Topic Modeling (7 papers) and Natural Language Processing Techniques (6 papers). Surabhi Datta collaborates with scholars based in United States, India and United Kingdom. Surabhi Datta's co-authors include Kirk Roberts, Jingcheng Du, Hua Xu, Sarvesh Soni, Zongcheng Ji, Stephen Wu, Yang Xiang, Qiang Wei, Bo Zhao and Yuqi Si and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Journal of the American Medical Informatics Association.

In The Last Decade

Surabhi Datta

19 papers receiving 466 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning in clinical natural language processing: a methodical review

2019 291 citations Stephen Wu, Kirk Roberts et al. Journal of the American Medical Informatics Association profile →

Peers

Surabhi Datta

RNMI

HIM

Joshua Levy United States

Lin Guo China

Saeed Mehrabi United States

Malek Hammou Norway

Khalid Gaffer Mohamed Saudi Arabia

Joy T. Wu United States

Saurabh Johri United Kingdom

Niels Olson United States

Peter Schulam United States

Joshua Levy United States

Surabhi Datta

196 ×0.8

155 ×1.2

102 ×1.2

RNMI

72 ×0.9

15 ×0.3

HIM

Citations per year, relative to Surabhi Datta Surabhi Datta (= 1×) peers Joshua Levy

Countries citing papers authored by Surabhi Datta

Since Specialization

Citations

This map shows the geographic impact of Surabhi Datta'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 Surabhi Datta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Surabhi Datta more than expected).

Fields of papers citing papers by Surabhi Datta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Surabhi Datta. 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 Surabhi Datta. The network helps show where Surabhi Datta may publish in the future.

Co-authorship network of co-authors of Surabhi Datta

This figure shows the co-authorship network connecting the top 25 collaborators of Surabhi Datta. A scholar is included among the top collaborators of Surabhi Datta 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 Surabhi Datta. Surabhi Datta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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19 of 19 papers shown

Li, Ying, Surabhi Datta, Majid Rastegar-Mojarad, et al.. (2025). Enhancing systematic literature reviews with generative artificial intelligence: development, applications, and performance evaluation. Journal of the American Medical Informatics Association. 32(4). 616–625. 4 indexed citations

Datta, Surabhi, Liang‐Chin Huang, Jonathan Gold, et al.. (2025). Patient2Trial: From patient to participant in clinical trials using large language models. Informatics in Medicine Unlocked. 53. 101615–101615. 1 indexed citations

Huang, Liang‐Chin, Surabhi Datta, Mitchell K. Higashi, et al.. (2024). Unveiling consistency: A large-scale analysis of conference proceedings and subsequent publications in oncology clinical trials using large language models.. Journal of Clinical Oncology. 42(16_suppl). 7568–7568. 1 indexed citations

Huang, Liang‐Chin, Surabhi Datta, Samuel M. Rubinstein, et al.. (2024). SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials. Informatics in Medicine Unlocked. 50. 101589–101589. 7 indexed citations

Plasek, Joseph M., Ya‐Wen Chuang, Liqin Wang, et al.. (2024). Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes. EBioMedicine. 109. 105401–105401. 15 indexed citations

Datta, Surabhi, Frank J. Manion, Jingcheng Du, et al.. (2023). AutoCriteria: a generalizable clinical trial eligibility criteria extraction system powered by large language models. Journal of the American Medical Informatics Association. 31(2). 375–385. 31 indexed citations

Pachade, Samiksha, Surabhi Datta, Yi Dong, et al.. (2023). Self-Supervised Learning with Radiology Reports, A Comparative Analysis of Strategies for Large Vessel Occlusion and Brain CTA Images. PubMed. 2023. 1–5. 3 indexed citations

Datta, Surabhi & Kirk Roberts. (2023). Weakly supervised spatial relation extraction from radiology reports. JAMIA Open. 6(2). ooad027–ooad027. 6 indexed citations

Soni, Sarvesh, Surabhi Datta, & Kirk Roberts. (2023). quEHRy: a question answering system to query electronic health records. Journal of the American Medical Informatics Association. 30(6). 1091–1102. 5 indexed citations

10.

Datta, Surabhi & Kirk Roberts. (2021). Fine-grained spatial information extraction in radiology as two-turn question answering. International Journal of Medical Informatics. 158. 104628–104628. 13 indexed citations

11.

Datta, Surabhi & Kirk Roberts. (2020). A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports. PubMed. 2020. 50–55. 12 indexed citations

12.

Datta, Surabhi & Kirk Roberts. (2020). A dataset of chest X-ray reports annotated with Spatial Role Labeling annotations. SHILAP Revista de lepidopterología. 32. 106056–106056. 6 indexed citations

13.

Datta, Surabhi, et al.. (2020). Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports.. PubMed. 2020. 2251–2260. 11 indexed citations

14.

Datta, Surabhi & Kirk Roberts. (2020). Spatial Relation Extraction from Radiology Reports using Syntax-Aware Word Representations.. PubMed. 2020. 116–125. 2 indexed citations

15.

Wu, Stephen, Kirk Roberts, Surabhi Datta, et al.. (2019). Deep learning in clinical natural language processing: a methodical review. Journal of the American Medical Informatics Association. 27(3). 457–470. 291 indexed citations breakdown →

16.

Datta, Surabhi, et al.. (2010). Composite MRI scores improve correlation with EDSS in multiple sclerosis. Multiple Sclerosis Journal. 16(9). 1117–1125. 30 indexed citations

17.

Datta, Surabhi, et al.. (2005). Adaptive FCM with contextual constrains for segmentation of multi-spectral MRI. PubMed. 3. 1660–1663. 10 indexed citations

18.

Datta, Surabhi, et al.. (2005). A unified approach for lesion segmentation on MRI of multiple sclerosis. PubMed. 3. 1778–1781. 10 indexed citations

19.

Gupta, Rakesh, Nuzhat Husain, Manoj Kathuria, et al.. (2001). Magnetization Transfer MR Imaging Correlation with Histopathology in Intracranial Tuberculomas. Clinical Radiology. 56(8). 656–663. 25 indexed citations

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.

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