Sharvari Shukla

11.1k total citations
23 papers, 290 citations indexed

About

Sharvari Shukla is a scholar working on Public Health, Environmental and Occupational Health, Infectious Diseases and Health Information Management. According to data from OpenAlex, Sharvari Shukla has authored 23 papers receiving a total of 290 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Public Health, Environmental and Occupational Health, 3 papers in Infectious Diseases and 3 papers in Health Information Management. Recurrent topics in Sharvari Shukla's work include COVID-19 epidemiological studies (3 papers), Artificial Intelligence in Healthcare (3 papers) and Big Data and Business Intelligence (2 papers). Sharvari Shukla is often cited by papers focused on COVID-19 epidemiological studies (3 papers), Artificial Intelligence in Healthcare (3 papers) and Big Data and Business Intelligence (2 papers). Sharvari Shukla collaborates with scholars based in India, Germany and United Kingdom. Sharvari Shukla's co-authors include Nishita Mehta, Anil Pandit, Revati Phalkey, Chittaranjan S. Yajnik, Rashmi B. Prasad, Olof Asplund, Michael Marx, Emma Ahlqvist, Carsten Butsch and Sanat Phatak and has published in prestigious journals such as SHILAP Revista de lepidopterología, Diabetologia and BMC Public Health.

In The Last Decade

Sharvari Shukla

19 papers receiving 279 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Sharvari Shukla India 8 54 53 48 46 41 23 290
Anna Ostropolets United States 11 19 0.4× 51 1.0× 57 1.2× 25 0.5× 64 1.6× 33 459
Amit Acharya United States 17 18 0.3× 94 1.8× 44 0.9× 37 0.8× 66 1.6× 47 574
Jungwei Fan United States 11 56 1.0× 82 1.5× 35 0.7× 37 0.8× 288 7.0× 51 533
Christophe Gaudet-Blavignac Switzerland 8 31 0.6× 37 0.7× 31 0.6× 7 0.2× 102 2.5× 36 310
Jessica Gronsbell Canada 11 14 0.3× 57 1.1× 45 0.9× 13 0.3× 149 3.6× 25 423
Rubina Rizvi United States 11 17 0.3× 50 0.9× 21 0.4× 10 0.2× 43 1.0× 29 259
Mohammad Mehrtak Iran 11 24 0.4× 12 0.2× 17 0.4× 19 0.4× 25 0.6× 26 351
Maryam Y. Garza United States 8 25 0.5× 104 2.0× 40 0.8× 7 0.2× 85 2.1× 33 293
Pierre Elias United States 11 96 1.8× 32 0.6× 29 0.6× 21 0.5× 114 2.8× 28 460
Shannan N. Rich United States 9 36 0.7× 17 0.3× 61 1.3× 6 0.1× 98 2.4× 28 388

Countries citing papers authored by Sharvari Shukla

Since Specialization
Citations

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

Fields of papers citing papers by Sharvari Shukla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sharvari Shukla

This figure shows the co-authorship network connecting the top 25 collaborators of Sharvari Shukla. A scholar is included among the top collaborators of Sharvari Shukla 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 Sharvari Shukla. Sharvari Shukla 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
1.
Shukla, Sharvari, et al.. (2025). Comparative Analysis of Machine Learning Models for Early Heart Disease Diagnosis. International Journal of Statistics in Medical Research. 14. 590–600.
2.
Elbatal, Ibrahim, et al.. (2025). Improving Alzheimer’s Disease Detection with Transfer Learning. International Journal of Statistics in Medical Research. 14. 403–415. 1 indexed citations
3.
4.
Viswanathan, Vijay, Amit Gupta, Sharvari Shukla, et al.. (2024). Early screening for foot problems in people with diabetes is the need of the hour: ‘Save the Feet and Keep Walking Campaign’ in India. BMJ Open Diabetes Research & Care. 12(4). e004064–e004064. 1 indexed citations
5.
Yajnik, Chittaranjan S., et al.. (2023). Polygenic scores of diabetes-related traits in subgroups of type 2 diabetes in India: a cohort study. The Lancet Regional Health - Southeast Asia. 14. 100182–100182. 14 indexed citations
6.
Pandya, Pranav, et al.. (2022). A Sensor Placement Strategy for Comprehensive Urban Heat Island Monitoring. ISPRS International Journal of Geo-Information. 12(1). 11–11. 2 indexed citations
7.
Brashier, Bill, et al.. (2022). Assessment of the predictive capability of modelling and simulation to determine bioequivalence of inhaled drugs: A systematic review. DARU Journal of Pharmaceutical Sciences. 30(1). 229–243. 1 indexed citations
8.
Prasad, Rashmi B., Olof Asplund, Sharvari Shukla, et al.. (2021). Subgroups of patients with young-onset type 2 diabetes in India reveal insulin deficiency as a major driver. Diabetologia. 65(1). 65–78. 52 indexed citations
9.
Mehta, Nishita & Sharvari Shukla. (2021). Pandemic Analytics: How Countries are Leveraging Big Data Analytics and Artificial Intelligence to Fight COVID-19?. SN Computer Science. 3(1). 54–54. 23 indexed citations
10.
Shukla, Urvi, et al.. (2021). Characteristics of COVID-19 Patients Admitted to a Tertiary Care Hospital in Pune, India and Predictors of Requirement for Intensive Care Treatment.. PubMed. 69(7). 11–12. 1 indexed citations
11.
Shukla, Sharvari, et al.. (2020). Determinants of health-promoting lifestyles amongst Indian University students. International Journal of Health Promotion and Education. 59(3). 135–144. 9 indexed citations
12.
Bhattacharjee, Atanu, et al.. (2020). Disease progression of cancer patients during COVID-19 pandemic: a comprehensive analytical strategy by time-dependent modelling. BMC Medical Research Methodology. 20(1). 209–209. 7 indexed citations
13.
Yeravdekar, Rajiv, et al.. (2019). Perception and Practices of Voluntary Blood Donation Amongst Students of Higher Educational Institutes in India. SHILAP Revista de lepidopterología. 2 indexed citations
14.
Mehta, Nishita, Anil Pandit, & Sharvari Shukla. (2019). Transforming healthcare with big data analytics and artificial intelligence: A systematic mapping study. Journal of Biomedical Informatics. 100. 103311–103311. 114 indexed citations
15.
Jasti, Pratima, et al.. (2017). Risk Assessment of Pesticide Residues in Selected Chilli Samples by Chromatography and Mass Spectrometry. Indian Journal of Public Health Research & Development. 8(4). 535–535. 4 indexed citations
16.
Yeravdekar, Rajiv, et al.. (2017). Trends in Students’ Outlook for Annual Health Checkup at an Indian University. Indian Journal of Public Health Research & Development. 8(4). 662–662.
17.
18.
Phalkey, Revati, et al.. (2016). Involving private healthcare practitioners in an urban NCD sentinel surveillance system: lessons learned from Pune, India. Global Health Action. 9(1). 32635–32635. 4 indexed citations
19.
Phalkey, Revati, et al.. (2015). Knowledge, attitude, and practices with respect to disease surveillance among urban private practitioners in Pune, India. Global Health Action. 8(1). 28413–28413. 14 indexed citations
20.
Phalkey, Revati, et al.. (2013). Assessment of the core and support functions of the Integrated Disease Surveillance system in Maharashtra, India. BMC Public Health. 13(1). 575–575. 34 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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