William Marsh

67 papers receiving 1.1k citations

Peers

William Marsh
Comparison fields: 5 of 152
  • Software 261
  • Health Informatics 28
  • Information Systems 304
  • Statistics, Probability and Uncertainty 88
  • Critical Care and Intensive Care Medicine 49
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Peter Lucas Netherlands
Dario A. Giuse United States
Mei Liu China
Ramesh S. Patil United States
Ibrahim Habli United Kingdom
Ellen J. Bass United States
Mark Hoogendoorn Netherlands
Stefania Montani Italy
Lawrence M. Fagan United States
Aaron Brown United States
William Marsh relative to Peter Lucas Netherlands Peter Lucas's profile →
Citations per field
00.5×2.8×
Peter Lucas · 1×
Citations per year

Countries citing papers authored by William Marsh

Since Specialization
Citations

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

Fields of papers citing papers by William Marsh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside William Marsh, 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 William Marsh Line = papers co-authored together William Marsh links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 71 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2006132
2 2016105
3 200893
4 201575
5 200453
6 201348
7 201241
8 201838
9 202133
10 201531
11 202130
12 200730
13 202029
14 201427
15 201527
16 202025
17 202024
18 202322
19 202020
20
Understanding congestive heart failure and self-administration of digoxin.
197219

About William Marsh

William Marsh is a scholar working on Artificial Intelligence, Health Information Management, Statistics, Probability and Uncertainty, Information Systems and Computer Science Applications, having authored 71 papers that have together received 1.2k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (18 papers), Electronic Health Records Systems (8 papers), Machine Learning in Healthcare (6 papers), Risk and Safety Analysis (5 papers), Software Engineering Research (5 papers), Teaching and Learning Programming (5 papers), Software Reliability and Analysis Research (5 papers) and Rheumatoid Arthritis Research and Therapies (4 papers). The work is most often cited by research in Software (261 citations), Health Informatics (28 citations), Information Systems (304 citations), Statistics, Probability and Uncertainty (88 citations) and Critical Care and Intensive Care Medicine (49 citations). William Marsh has collaborated with scholars based in United Kingdom, United States and Türkiye. Frequent co-authors include Norman Fenton, Martin Neil, Barbaros Yet, Nigel Tai, Łukasz Radliński, Anthony C. Constantinou, Zane Perkins, Paul Krause, Peter Hearty and Todd E. Rasmussen. Their work appears in journals such as Artificial Intelligence in Medicine, Journal of Biomedical Informatics, International Journal of Medical Informatics, Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability and Annals of Surgery.

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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