Sanjoy Kumer Dey

1.4k citations
65 papers · 728 indexed · h-index 13
Topics
Neonatal Respiratory Health Research (12 papers)Machine Learning in Healthcare (11 papers)Neonatal Health and Biochemistry (5 papers)
Journals
Nucleic Acids ResearchSHILAP Revista de lepidopterologíaPLoS ONE

In The Last Decade

Sanjoy Kumer Dey

55 papers receiving 703 citations

Peers

Sanjoy Kumer Dey
Comparison fields: 5 of 117
  • Molecular Biology 245
  • Computational Theory and Mathematics 220
  • Artificial Intelligence 117
  • Pulmonary and Respiratory Medicine 112
  • Epidemiology 95
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Countries citing papers authored by Sanjoy Kumer Dey

Since Specialization
Citations

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

Fields of papers citing papers by Sanjoy Kumer Dey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sanjoy Kumer Dey. 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 Sanjoy Kumer Dey. The network helps show where Sanjoy Kumer Dey may publish in the future.

Co-authorship network of co-authors of Sanjoy Kumer Dey

This figure shows the co-authorship network connecting the top 25 collaborators of Sanjoy Kumer Dey. A scholar is included among the top collaborators of Sanjoy Kumer Dey 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 Sanjoy Kumer Dey. Sanjoy Kumer Dey 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 1
2 0
3 21
4 4
5 4
6 5
7 108
8
Predicting Neonatal Hyperbilirubinemia Using First Day Serum Bilirubin Level in Late Preterm and Term Healthy Newborn.
1
9 1
10
Predicting the Factors of Improvement of Health Status of Home Health Care Patients: A Holistic Data Mining Approach.
1
11
Clustering Health Data to Discover EBP Interventions for Sepsis Prevention and Treatment for Health Disparities.
1
12 8
13
Data Mining Methodologies to Discover Best practices for Diabetic Patients with Health Disparities.
1
14 38
15
Data Mining to Predict Mobility Outcomes for Older Adults Receiving Home Health Care.
1
16 9
17 26
18 52
19 1
20
A supervised independent-living orientation program for adolescents.
7

About Sanjoy Kumer Dey

Sanjoy Kumer Dey is a scholar working on Health Informatics, Health Information Management and Pediatrics, Perinatology and Child Health, having authored 65 papers that have together received 728 indexed citations. Recurring topics across this work include Neonatal Respiratory Health Research (12 papers), Machine Learning in Healthcare (11 papers) and Neonatal Health and Biochemistry (5 papers). The work is most often cited by research in Health Informatics (38 citations), Health Information Management (82 citations) and Computational Theory and Mathematics (220 citations). Sanjoy Kumer Dey has collaborated with scholars based in Bangladesh, United States and Pakistan. Frequent co-authors include Ping Zhang, Heng Luo, Achille Fokoue, Arun Iyengar, Jianying Hu, Vipin Kumar, Kenney Ng, Christopher R. deFilippi, Walter F. Stewart and Steven R. Steinhubl. Their work appears in journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and PLoS ONE.

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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