Hoyt Burdick

449 total citations
8 papers, 269 citations indexed

About

Hoyt Burdick is a scholar working on Epidemiology, Artificial Intelligence and Infectious Diseases. According to data from OpenAlex, Hoyt Burdick has authored 8 papers receiving a total of 269 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Epidemiology, 4 papers in Artificial Intelligence and 3 papers in Infectious Diseases. Recurrent topics in Hoyt Burdick's work include Sepsis Diagnosis and Treatment (4 papers), SARS-CoV-2 and COVID-19 Research (3 papers) and COVID-19 Clinical Research Studies (3 papers). Hoyt Burdick is often cited by papers focused on Sepsis Diagnosis and Treatment (4 papers), SARS-CoV-2 and COVID-19 Research (3 papers) and COVID-19 Clinical Research Studies (3 papers). Hoyt Burdick collaborates with scholars based in United States and Belgium. Hoyt Burdick's co-authors include Ritankar Das, Emily Pellegrini, Jana Hoffman, Gina Barnes, Abigail Green‐Saxena, Jacob Calvert, Anna Siefkas, Andrea J. McCoy, Gregory L. Braden and Samson Mataraso and has published in prestigious journals such as Applied Sciences, Clinical Therapeutics and Computers in Biology and Medicine.

In The Last Decade

Hoyt Burdick

8 papers receiving 254 citations

Peers

Hoyt Burdick

AI

RNMI

EPIDE

HI

ID

Gina Barnes United States

Jeremy A. Balch United States

Arash Kia United States

Anna Siefkas United States

Davy van de Sande Netherlands

Markus Lingman Sweden

Andreas Coppi United States

Nianzong Hou China

Marta Fernandes United States

Jens Schierbeck Denmark

Gina Barnes United States

Hoyt Burdick

129 ×1.3

AI

109 ×1.2

RNMI

104 ×1.4

EPIDE

78 ×1.3

HI

43 ×1.3

ID

Citations per year, relative to Hoyt Burdick Hoyt Burdick (= 1×) peers Gina Barnes

Countries citing papers authored by Hoyt Burdick

Since Specialization

Citations

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

Fields of papers citing papers by Hoyt Burdick

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hoyt Burdick

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

All Works

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8 of 8 papers shown

1.

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

2.

Lam, Carson, Anna Siefkas, Nicole S. Zelin, et al.. (2021). Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids. Clinical Therapeutics. 43(5). 871–885. 11 indexed citations

3.

Allen, Angier, Anna Siefkas, Emily Pellegrini, et al.. (2021). A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients. Applied Sciences. 11(12). 5576–5576. 43 indexed citations

4.

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

5.

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

6.

Burdick, Hoyt, Carol Gu, Jonathan Roberts, et al.. (2020). Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Medical Informatics and Decision Making. 20(1). 276–276. 29 indexed citations

7.

Burdick, Hoyt, Andrea J. McCoy, Carol Gu, et al.. (2020). Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals. BMJ Health & Care Informatics. 27(1). e100109–e100109. 48 indexed citations

8.

Burdick, Hoyt, Carson Lam, Samson Mataraso, et al.. (2020). Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial. Journal of Clinical Medicine. 9(12). 3834–3834. 6 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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