William John Long

212 papers receiving 3.9k citations

Peers

William John Long
Comparison fields: 5 of 185
  • Virology 250
  • Health Information Management 224
  • Health Informatics 69
  • Surgery 1.6k
  • Artificial Intelligence 682
Replace Daniel R. Masys with:
Daniel R. Masys United States
Leslie Andrew Lenert United States
David A. Hanauer United States
Maurice Mars South Africa
Rui Tato Marinho Portugal
David F. Lobach United States
Piet J.M. Bakker Netherlands
Andreas A. Theodorou United States
Faraz S. Ahmad United States
Abel Kho United States
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Citations per year

Countries citing papers authored by William John Long

Since Specialization
Citations

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

Fields of papers citing papers by William John Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 228 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008320
2 2019158
3 2002120
4 2008120
5 1993111
6 1993102
7 2019101
8 1992101
9 202188
10 201385
11 201484
12
A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients.
199578
13
Using classification tree and logistic regression methods to diagnose myocardial infarction.
199871
14 200871
15 201769
16
Risk stratification of ICU patients using topic models inferred from unstructured progress notes.
201267
17
Guardian Angel: Patient-Centered Health Information Systems
199461
18 199457
19 199454
20 202051

About William John Long

William John Long is a scholar working on Surgery, Health Information Management, Artificial Intelligence, Development and Public Health, Environmental and Occupational Health, having authored 228 papers that have together received 4.2k indexed citations. Recurring topics across this work include Total Knee Arthroplasty Outcomes (69 papers), Orthopaedic implants and arthroplasty (38 papers), Orthopedic Infections and Treatments (31 papers), Machine Learning in Healthcare (10 papers), Opioid Use Disorder Treatment (10 papers), AI-based Problem Solving and Planning (9 papers), Biomedical Text Mining and Ontologies (9 papers) and International Relations and Foreign Policy (8 papers). The work is most often cited by research in Virology (250 citations), Health Information Management (224 citations), Health Informatics (69 citations), Surgery (1.6k citations) and Artificial Intelligence (682 citations). William John Long has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Ran M. Schwarzkopf, Giles R. Scuderi, Peter Brecke, Peter Szolovits, Jonathan M. Vigdorchik, Richard R. Iorio, Andrew Tomas Reisner, Roger G. Mark, George B Moody and Li-wei H. Lehman. Their work appears in journals such as The Journal of Arthroplasty, Arthroplasty Today, Archives of Orthopaedic and Trauma Surgery, Artificial Intelligence in Medicine and The Bone & Joint Journal.

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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