Jacob Calvert

3.2k total citations · 1 hit paper
57 papers, 2.1k citations indexed

About

Jacob Calvert is a scholar working on Epidemiology, Artificial Intelligence and Surgery. According to data from OpenAlex, Jacob Calvert has authored 57 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Epidemiology, 16 papers in Artificial Intelligence and 9 papers in Surgery. Recurrent topics in Jacob Calvert's work include Sepsis Diagnosis and Treatment (24 papers), Machine Learning in Healthcare (15 papers) and Respiratory Support and Mechanisms (4 papers). Jacob Calvert is often cited by papers focused on Sepsis Diagnosis and Treatment (24 papers), Machine Learning in Healthcare (15 papers) and Respiratory Support and Mechanisms (4 papers). Jacob Calvert collaborates with scholars based in United States, United Kingdom and Belgium. Jacob Calvert's 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 and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Circulation.

In The Last Decade

Jacob Calvert

54 papers receiving 2.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

2016 339 citations Thomas Desautels, Jacob Calvert et al. JMIR Medical Informatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jacob Calvert United States	23	1.0k	928	273	251	238	57	2.1k
Ritankar Das United States	27	1.3k 1.3×	1.2k 1.3×	372 1.4×	343 1.4×	348 1.5×	64	2.6k
Jana Hoffman United States	22	931 0.9×	840 0.9×	277 1.0×	229 0.9×	238 1.0×	41	1.7k
Steven Horng United States	14	657 0.6×	1.2k 1.3×	607 2.2×	226 0.9×	298 1.3×	34	2.8k
Omar Badawi United States	20	1.1k 1.0×	854 0.9×	155 0.6×	469 1.9×	224 0.9×	53	2.7k
Matthieu Komorowski United Kingdom	20	470 0.4×	694 0.7×	431 1.6×	241 1.0×	156 0.7×	63	2.3k
Karandeep Singh United States	27	427 0.4×	572 0.6×	266 1.0×	152 0.6×	234 1.0×	106	2.6k
Jenna Wiens United States	26	396 0.4×	775 0.8×	396 1.5×	191 0.8×	298 1.3×	81	2.4k
Uli K. Chettipally United States	21	888 0.8×	709 0.8×	232 0.8×	325 1.3×	203 0.9×	42	1.9k
Jie Ma China	22	449 0.4×	744 0.8×	485 1.8×	343 1.4×	293 1.2×	120	3.2k
Andre L. Holder United States	15	678 0.6×	480 0.5×	147 0.5×	363 1.4×	88 0.4×	39	1.4k

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-authorship network of co-authors of Jacob Calvert

This figure shows the co-authorship network connecting the top 25 collaborators of Jacob Calvert. A scholar is included among the top collaborators of Jacob Calvert 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 Jacob Calvert. Jacob Calvert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Thapa, Rahul, Anurag Garikipati, Sepideh Shokouhi, et al.. (2022). Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging. 5(2). e35373–e35373. 22 indexed citations

Le, Sidney, Abigail Green‐Saxena, Jacob Calvert, et al.. (2022). Mortality, disease progression, and disease burden of acute kidney injury in alcohol use disorder subpopulation. The American Journal of the Medical Sciences. 364(1). 46–52. 2 indexed citations

Shokouhi, Sepideh, et al.. (2022). Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients. Thrombosis Research. 216. 14–21. 11 indexed citations

Mataraso, Samson, Gina Barnes, Sepideh Shokouhi, et al.. (2022). Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm. Frontiers in Neurology. 12. 784250–784250. 3 indexed citations

Lam, Carson, Chak Foon Tso, Abigail Green‐Saxena, et al.. (2021). Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study. JMIR Formative Research. 5(9). e28028–e28028. 13 indexed citations

Lam, Charles, Anna Siefkas, Nicole S. Zelin, et al.. (2021). Using Machine Learning as a Precision Medicine Approach for Remdesivir and Corticosteroids as COVID-19 Pharmacotherapies. Clinical Therapeutics. 1 indexed citations

Garikipati, Anurag, Nicole S. Zelin, Emily Pellegrini, et al.. (2021). Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data. BioData Mining. 14(1). 23–23. 10 indexed citations

Rahmani, Keyvan, Anurag Garikipati, Gina Barnes, et al.. (2021). Early prediction of central line associated bloodstream infection using machine learning. American Journal of Infection Control. 50(4). 440–445. 24 indexed citations

Allen, Angier, Samson Mataraso, Anna Siefkas, et al.. (2020). A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study. JMIR Public Health and Surveillance. 6(4). e22400–e22400. 35 indexed citations

10.

Burdick, Hoyt, Carson Lam, Samson Mataraso, et al.. (2020). Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Computers in Biology and Medicine. 124. 103949–103949. 96 indexed citations

11.

Le, Sidney, Jana Hoffman, Christopher Barton, et al.. (2019). Pediatric Severe Sepsis Prediction Using Machine Learning. Frontiers in Pediatrics. 7. 413–413. 83 indexed citations

12.

Mao, Qingqing, Melissa Jay, Jana Hoffman, et al.. (2018). Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 8(1). e017833–e017833. 229 indexed citations

13.

Mohamadlou, Hamid, Christopher Barton, Uli K. Chettipally, et al.. (2018). Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data. Canadian Journal of Kidney Health and Disease. 5. 2246655494–2246655494. 102 indexed citations

14.

Desautels, Thomas, Ritankar Das, Jacob Calvert, et al.. (2017). Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach. BMJ Open. 7(9). e017199–e017199. 83 indexed citations

15.

Desautels, Thomas, Jacob Calvert, Jana Hoffman, et al.. (2016). Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Medical Informatics. 4(3). e28–e28. 339 indexed citations breakdown →

16.

Calvert, Jacob, Qingqing Mao, Angela J. Rogers, et al.. (2016). A computational approach to mortality prediction of alcohol use disorder inpatients. Computers in Biology and Medicine. 75. 74–79. 15 indexed citations

17.

Calvert, Jacob, Uli K. Chettipally, Christopher W. Barton, et al.. (2016). A computational approach to early sepsis detection. Computers in Biology and Medicine. 74. 69–73. 184 indexed citations

18.

Calvert, Jacob, Thomas Desautels, Uli K. Chettipally, et al.. (2016). High-performance detection and early prediction of septic shock for alcohol-use disorder patients. Annals of Medicine and Surgery. 8. 50–55. 42 indexed citations

19.

Dorais, Jessie, Mark Dodson, Jacob Calvert, et al.. (2011). Fertility-Sparing Management of Endometrial Adenocarcinoma. Obstetrical & Gynecological Survey. 66(7). 443–451. 17 indexed citations

20.

Hussain, Syed A., Deborah Katten, Alan W. Ahlberg, et al.. (2004). 816-5 Progression of coronary artery disease in diabetics demonstrated by single isotope rest/stress Tc-99m sestamibi myocardial perfusion imaging: More rapid than nondiabetics?. Journal of the American College of Cardiology. 43(5). A340–A340. 2 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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