Jacob Calvert

54 papers receiving 2.0k citations

Jacob Calvert's Hit Papers

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach 2016 · 349 citations
3490+3+6Years since publication100200300

Peers

Jacob Calvert
Comparison fields: 5 of 135
  • Health Informatics 171
  • Family Practice 99
  • Health Information Management 170
  • Epidemiology 847
  • Artificial Intelligence 606
Replace Jana Hoffman with:
Jana Hoffman United States
Ritankar Das United States
Uli K. Chettipally United States
Omar Badawi United States
Steven Horng United States
Qingqing Mao United States
David N. Hager United States
Glen P. Martin United Kingdom
Brian W. Pickering United States
Jesse D. Raffa United States
Jacob Calvert relative to Jana Hoffman United States Jana Hoffman's profile →
Citations per field
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Jana Hoffman · 1×
Citations per year

Countries citing papers authored by Jacob Calvert

Since Specialization
Citations

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

Fields of papers citing papers by Jacob Calvert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jacob Calvert, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jacob Calvert Line = papers co-authored together Jacob Calvert links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 57 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach
Hit paper breakdown →
2016349
2 2018235
3 2016188
4 2019121
5 2018105
6 202097
7 201784
8 201983
9 201574
10 202061
11 201458
12 201651
13 202144
14 202142
15 201642
16 201740
17 202035
18 202134
19 202128
20 202127

About Jacob Calvert

Jacob Calvert is a scholar working on Epidemiology, Artificial Intelligence, Cardiology and Cardiovascular Medicine, Infectious Diseases and Internal Medicine, having authored 57 papers that have together received 2.1k indexed citations. Recurring topics across this work include Sepsis Diagnosis and Treatment (12 papers), Machine Learning in Healthcare (5 papers), Venous Thromboembolism Diagnosis and Management (4 papers), COVID-19 Clinical Research Studies (3 papers), COVID-19 diagnosis using AI (3 papers), RNA Interference and Gene Delivery (2 papers), Stochastic processes and statistical mechanics (2 papers) and Geophysical Methods and Applications (2 papers). The work is most often cited by research in Health Informatics (171 citations), Family Practice (99 citations), Health Information Management (170 citations), Epidemiology (847 citations) and Artificial Intelligence (606 citations). Jacob Calvert has collaborated with scholars based in United States, United Kingdom and Belgium. Frequent co-authors include Ritankar Das, Jana Hoffman, Uli K. Chettipally, Melissa Jay, Qingqing Mao, Christopher Barton, Lisa Shieh, Thomas Desautels, Yaniv Kerem and Mitchell D. Feldman. Their work appears in journals such as Computers in Biology and Medicine, BMJ Open, Nucleic Acids Research, American Journal of Infection Control and Critical Care Medicine.

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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