Madhumita Sushil

18 papers and 163 indexed citations i.

About

Madhumita Sushil is a scholar working on Artificial Intelligence, Health Informatics and Molecular Biology. According to data from OpenAlex, Madhumita Sushil has authored 18 papers receiving a total of 163 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 9 papers in Health Informatics and 5 papers in Molecular Biology. Recurrent topics in Madhumita Sushil’s work include Artificial Intelligence in Healthcare and Education (9 papers), Machine Learning in Healthcare (8 papers) and Topic Modeling (8 papers). Madhumita Sushil is often cited by papers focused on Artificial Intelligence in Healthcare and Education (9 papers), Machine Learning in Healthcare (8 papers) and Topic Modeling (8 papers). Madhumita Sushil collaborates with scholars based in United States, Belgium and Germany. Madhumita Sushil's co-authors include Atul J. Butte, Brenda Y. Miao, Walter Daelemans, Simon Šuster, Kim Luyckx, Travis Zack, Ahmed M. Alaa, Christopher Y. K. Williams, Aaron E. Kornblith and Vanessa E. Kennedy and has published in prestigious journals such as Nature Medicine, Cancer Research and Journal of Clinical Epidemiology.

In The Last Decade

Co-authorship network of co-authors of Madhumita Sushil i

Fields of papers citing papers by Madhumita Sushil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Madhumita Sushil

Since Specialization
Citations

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

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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