James McNicholas

607 total citations
10 papers, 309 citations indexed

About

James McNicholas is a scholar working on Epidemiology, Artificial Intelligence and Emergency Medicine. According to data from OpenAlex, James McNicholas has authored 10 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Epidemiology, 5 papers in Artificial Intelligence and 5 papers in Emergency Medicine. Recurrent topics in James McNicholas's work include Machine Learning in Healthcare (5 papers), Sepsis Diagnosis and Treatment (5 papers) and Trauma, Hemostasis, Coagulopathy, Resuscitation (4 papers). James McNicholas is often cited by papers focused on Machine Learning in Healthcare (5 papers), Sepsis Diagnosis and Treatment (5 papers) and Trauma, Hemostasis, Coagulopathy, Resuscitation (4 papers). James McNicholas collaborates with scholars based in United Kingdom, Austria and Egypt. James McNicholas's co-authors include Mohamed Bader–El–Den, Aya Awad, Jim Briggs, Yasser El-Sonbaty, David Inwald, G. Suren Arul, Kelly E. Wood, KL Woods, PF Mahoney and Tanja Stamm and has published in prestigious journals such as BMC Public Health, International Journal of Medical Informatics and Health Informatics Journal.

In The Last Decade

James McNicholas

8 papers receiving 300 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James McNicholas United Kingdom 7 143 105 66 50 42 10 309
Min‐Jeoung Kang United States 11 92 0.6× 104 1.0× 98 1.5× 58 1.2× 35 0.8× 29 361
Jennifer C. Ginestra United States 6 181 1.3× 216 2.1× 49 0.7× 54 1.1× 73 1.7× 16 366
Nancy Gentry United States 3 103 0.7× 67 0.6× 85 1.3× 19 0.4× 59 1.4× 4 421
Jonathan Austrian United States 14 64 0.4× 75 0.7× 94 1.4× 65 1.3× 73 1.7× 25 532
Shaun T Alfreds United States 8 134 0.9× 117 1.1× 101 1.5× 67 1.3× 52 1.2× 12 455
Heather Gardner United States 8 81 0.6× 104 1.0× 50 0.8× 188 3.8× 49 1.2× 14 410
Devore S Culver United States 6 109 0.8× 99 0.9× 88 1.3× 67 1.3× 33 0.8× 10 352
Frank Stearns United States 11 158 1.1× 148 1.4× 117 1.8× 87 1.7× 58 1.4× 14 579
H.M. Giannini United States 4 164 1.1× 194 1.8× 46 0.7× 35 0.7× 71 1.7× 8 340
Samson Mataraso United States 10 219 1.5× 218 2.1× 83 1.3× 31 0.6× 109 2.6× 22 559

Countries citing papers authored by James McNicholas

Since Specialization
Citations

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

Fields of papers citing papers by James McNicholas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James McNicholas

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

All Works

10 of 10 papers shown
1.
Stamm, Tanja, Mohamed Bader–El–Den, James McNicholas, Jim Briggs, & Peng Zhao. (2025). Applications of generative artificial intelligence in outcome prediction in intensive care medicine—a scoping review. Frontiers in Digital Health. 7. 1633458–1633458.
2.
Bader–El–Den, Mohamed, et al.. (2019). Using the National Early Warning Score (NEWS/NEWS 2) in different Intensive Care Units (ICUs) to predict the discharge location of patients. BMC Public Health. 19(1). 1231–1231. 18 indexed citations
3.
Awad, Aya, Mohamed Bader–El–Den, James McNicholas, Jim Briggs, & Yasser El-Sonbaty. (2019). Predicting hospital mortality for intensive care unit patients: Time-series analysis. Health Informatics Journal. 26(2). 1043–1059. 35 indexed citations
4.
Awad, Aya, Mohamed Bader–El–Den, James McNicholas, & Jim Briggs. (2017). Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. International Journal of Medical Informatics. 108. 185–195. 125 indexed citations
5.
Awad, Aya, Mohamed Bader–El–Den, & James McNicholas. (2017). Patient length of stay and mortality prediction: A survey. Health Services Management Research. 30(2). 105–120. 88 indexed citations
6.
McNicholas, James, et al.. (2015). Coma query cause. BMJ Case Reports. 2015. bcr2014205592–bcr2014205592. 1 indexed citations
7.
Inwald, David, et al.. (2013). Management of children in the deployed intensive care unit at Camp Bastion, Afghanistan. Journal of the Royal Army Medical Corps. 160(3). 236–240. 21 indexed citations
8.
Woods, KL, et al.. (2012). The Pattern of Paediatric Trauma on Operations. Journal of the Royal Army Medical Corps. 158(1). 34–37. 11 indexed citations
9.
McNicholas, James, et al.. (2011). Major Military Trauma: Decision Making in the ICU. Journal of the Royal Army Medical Corps. 157(Suppl 3). S284–S288.
10.
Wood, Kelly E., et al.. (2010). The Paediatric Transfusion Challenge on Deployed Operations. Journal of the Royal Army Medical Corps. 156(Suppl 4). S361–364. 10 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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