Ankit Sakhuja

1.9k citations
56 papers · 918 indexed · h-index 17
Topics
Sepsis Diagnosis and Treatment (7 papers)Acute Kidney Injury Research (6 papers)Machine Learning in Healthcare (6 papers)
Partner nations
United StatesIndiaIsrael

In The Last Decade

Ankit Sakhuja

53 papers receiving 894 citations

Peers

Ankit Sakhuja
Comparison fields: 5 of 106
  • Cardiology and Cardiovascular Medicine 327
  • Epidemiology 218
  • Surgery 205
  • Pulmonary and Respiratory Medicine 168
  • Infectious Diseases 103
Replace Elisabetta Cerutti with:
Elisabetta Cerutti Italy
Kannaiyan S Rabindranath United Kingdom
Christina Fitzner Germany
Marwan S. Abougergi United States
Natalie Yip United States
Jerry T. Dang Canada
Glenn Whitman United States
Fernando Rosell Ortiz Spain
Catherine Farrell Canada
David M. Axelrod United States
Ankit Sakhuja relative to Elisabetta Cerutti Italy Elisabetta Cerutti's profile →
Citations per field
00.5×3.3×
Elisabetta Cerutti · 1×
Citations per year

Countries citing papers authored by Ankit Sakhuja

Since Specialization
Citations

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

Fields of papers citing papers by Ankit Sakhuja

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ankit Sakhuja

This figure shows the co-authorship network connecting the top 25 collaborators of Ankit Sakhuja. A scholar is included among the top collaborators of Ankit Sakhuja 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 Ankit Sakhuja. Ankit Sakhuja 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 0
2 0
3 0
4 5
5 8
6 17
7 3
8 8
9 2
10 4
11 5
12 4
13 31
14 13
15 123
16 2
17 18
18 19
19 54
20 2

About Ankit Sakhuja

Ankit Sakhuja is a scholar working on Transplantation, Critical Care and Intensive Care Medicine and Nephrology, having authored 56 papers that have together received 918 indexed citations. Recurring topics across this work include Sepsis Diagnosis and Treatment (7 papers), Acute Kidney Injury Research (6 papers) and Machine Learning in Healthcare (6 papers). The work is most often cited by research in Health Informatics (26 citations), Critical Care and Intensive Care Medicine (89 citations) and Transplantation (44 citations). Ankit Sakhuja has collaborated with scholars based in United States, India and Israel. Frequent co-authors include Rahul Nanchal, Gagan Kumar, Roy C. Ziegelstein, Kapil Parakh, Nilay Kumar, Jeffrey B. Geske, Saraschandra Vallabhajosyula, Shipra Gupta, Ognjen Gajic and Rahul Kashyap. Their work appears in journals such as Nature Communications, Stroke and CHEST Journal.

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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