Janani Venugopalan

1.3k citations
22 papers · 790 indexed · 1 hit paper · h-index 11
Topics
Machine Learning in Healthcare (9 papers)Artificial Intelligence in Healthcare (4 papers)Stroke Rehabilitation and Recovery (3 papers)

In The Last Decade

Janani Venugopalan

22 papers receiving 757 citations

Hit Papers

Multimodal deep learning models for early detection of Al...20212026202220242021100200300400

Peers

Janani Venugopalan
Comparison fields: 5 of 110
  • Artificial Intelligence 309
  • Health Information Management 196
  • Neurology 167
  • Psychiatry and Mental health 121
  • Radiology, Nuclear Medicine and Imaging 114
Replace Maryam Panahiazar with:
Maryam Panahiazar United States
Zhenxing Xu United States
Vasileios C. Pezoulas Greece
Hager Saleh Egypt
C. Kavitha India
Domenico Diacono Italy
Paulo Mazzoncini de Azevedo‐Marques Brazil
Sorayya Rezayi Iran
Janani Venugopalan relative to Maryam Panahiazar United States Maryam Panahiazar's profile →
Citations per field
00.5×1.6×
Maryam Panahiazar · 1×
Citations per year

Countries citing papers authored by Janani Venugopalan

Since Specialization
Citations

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

Fields of papers citing papers by Janani Venugopalan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Janani Venugopalan

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 6
2 17
3
Multimodal deep learning models for early detection of Alzheimer’s disease stagebreakdown →
404
4 1
5 11
6 13
7 5
8 1
9 6
10 10
11 3
12 11
13 193
14 1
15 5
16 2
17 15
18 35
19 25
20 7

About Janani Venugopalan

Janani Venugopalan is a scholar working on Health Information Management, Rehabilitation and Applied Psychology, having authored 22 papers that have together received 790 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (9 papers), Artificial Intelligence in Healthcare (4 papers) and Stroke Rehabilitation and Recovery (3 papers). The work is most often cited by research in Health Informatics (64 citations), Health Information Management (196 citations) and Neurology (167 citations). Janani Venugopalan has collaborated with scholars based in United States, Russia and Cambodia. Frequent co-authors include May D. Wang, Hamid Reza Hassanzadeh, Tong Li, Chih‐Wen Cheng, Ryan Hoffman, Po-Yen Wu, Chanchala Kaddi, Nikhil K. Chanani, Kevin Maher and Todd H. Stokes. Their work appears in journals such as Scientific Reports, IEEE Transactions on Biomedical Engineering and IEEE Journal of Biomedical and Health Informatics.

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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