Wade L. Schulz

11.2k citations
69 papers · 1.8k · 1 hit paper · h-index 20

Impact in

Papers in

Wade L. Schulz

62 papers receiving 1.8k citations

Wade L. Schulz's Hit Papers

Blockchain Technology 2017 · 258 citations
2580+3+6Years since publication50100150200250

Peers

Wade L. Schulz
Comparison fields: 5 of 148
  • Health Informatics 73
  • Infectious Diseases 439
  • Health Information Management 77
  • Modeling and Simulation 62
  • Health 88
Replace Anand S. Dighe with:
Anand S. Dighe United States
Yukun Cao China
Vasa Ćurčin United Kingdom
Vida Abedi United States
Nathan Peiffer‐Smadja France
Smadar Shilo Israel
Andrea Dugas United States
Jane Snowdon United States
Karthik Natarajan United States
Kalyan S. Pasupathy United States
Wade L. Schulz relative to Anand S. Dighe United States Anand S. Dighe's profile →
Citations per field
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Citations per year

Countries citing papers authored by Wade L. Schulz

Since Specialization
Citations

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

Fields of papers citing papers by Wade L. Schulz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wade L. Schulz. 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 Wade L. Schulz. The network helps show where Wade L. Schulz may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 69 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Blockchain Technology
Hit paper breakdown →
2017258
2 2021196
3 2020182
4 2018100
5 201894
6 201280
7 202271
8 202159
9 202153
10 202153
11 201951
12 202041
13 201940
14 201737
15 201030
16 202324
17 201624
18 201923
19 201823
20 201520

About Wade L. Schulz

Wade L. Schulz is a scholar working on Infectious Diseases, Cardiology and Cardiovascular Medicine, Epidemiology, Artificial Intelligence and Molecular Biology, having authored 69 papers that have together received 1.8k indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (9 papers), COVID-19 Clinical Research Studies (5 papers), Machine Learning in Healthcare (5 papers), Health Systems, Economic Evaluations, Quality of Life (3 papers), Artificial Intelligence in Healthcare (3 papers), Cardiac, Anesthesia and Surgical Outcomes (2 papers), Blood Pressure and Hypertension Studies (2 papers) and Single-cell and spatial transcriptomics (2 papers). The work is most often cited by research in Health Informatics (73 citations), Infectious Diseases (439 citations), Health Information Management (77 citations), Modeling and Simulation (62 citations) and Health (88 citations). Wade L. Schulz has collaborated with scholars based in United States, Brazil and Spain. Frequent co-authors include Harlan M. Krumholz, Suveen Angraal, Thomas J S Durant, F. Perry Wilson, Adrian D. Haimovich, H. P. Young, Leslie A. Schiff, Amelia K. Haj, Richard Torres and Neal G. Ravindra. Their work appears in journals such as Clinical Chemistry, PLoS ONE, Nature Communications, Circulation Cardiovascular Quality and Outcomes and Journal of the American Heart Association.

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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