William J. Long

5.5k citations
214 papers · 3.7k · h-index 32

Impact in

Papers in

    • Total Knee Arthroplasty Outcomes 66
    • Orthopaedic implants and arthroplasty 38
    • Orthopedic Infections and Treatments 30
    • Machine Learning in Healthcare 10
    • Bayesian Modeling and Causal Inference 8
    • AI-based Problem Solving and Planning 8

William J. Long

203 papers receiving 3.5k citations

Peers

William J. Long
Comparison fields: 5 of 179
  • Virology 237
  • Health Informatics 62
  • Health Information Management 191
  • Surgery 1.4k
  • Artificial Intelligence 565
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Citations per field
00.5×10.9×
Leslie Lenert · 1×
Citations per year

Countries citing papers authored by William J. Long

Since Specialization
Citations

This map shows the geographic impact of William J. 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 J. 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 J. Long more than expected).

Fields of papers citing papers by William J. Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 214 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008281
2 2019152
3 2008109
4 2002105
5 1993100
6 199296
7 199395
8 201992
9 201477
10 201375
11
A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients.
199572
12 200870
13 201768
14
Using classification tree and logistic regression methods to diagnose myocardial infarction.
199864
15
Risk stratification of ICU patients using topic models inferred from unstructured progress notes.
201264
16 199457
17 201946
18 202046
19 199446
20
Guardian Angel: Patient-Centered Health Information Systems
199446

About William J. Long

William J. Long is a scholar working on Surgery, Artificial Intelligence, Molecular Biology, Public Health, Environmental and Occupational Health and Sociology and Political Science, having authored 214 papers that have together received 3.7k indexed citations. Recurring topics across this work include Total Knee Arthroplasty Outcomes (66 papers), Orthopaedic implants and arthroplasty (38 papers), Orthopedic Infections and Treatments (30 papers), Machine Learning in Healthcare (10 papers), Opioid Use Disorder Treatment (10 papers), Biomedical Text Mining and Ontologies (9 papers), Bayesian Modeling and Causal Inference (8 papers) and AI-based Problem Solving and Planning (8 papers). The work is most often cited by research in Virology (237 citations), Health Informatics (62 citations), Health Information Management (191 citations), Surgery (1.4k citations) and Artificial Intelligence (565 citations). William J. Long has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Ran Schwarzkopf, Giles R. Scuderi, Peter Szolovits, Peter Brecke, Jonathan M. Vigdorchik, Richard Iorio, Andrew Reisner, G.B. Moody, Roger G. Mark and Li-wei H. Lehman. Their work appears in journals such as The Journal of Arthroplasty, Arthroplasty Today, Archives of Orthopaedic and Trauma Surgery, The Bone & Joint Journal and JBJS Reviews.

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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