Wade L. Schulz

11.0k total citations · 3 hit papers
69 papers, 1.7k citations indexed

About

Wade L. Schulz is a scholar working on Infectious Diseases, Cardiology and Cardiovascular Medicine and Epidemiology. According to data from OpenAlex, Wade L. Schulz has authored 69 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Infectious Diseases, 16 papers in Cardiology and Cardiovascular Medicine and 14 papers in Epidemiology. Recurrent topics in Wade L. Schulz's work include SARS-CoV-2 and COVID-19 Research (10 papers), Machine Learning in Healthcare (9 papers) and COVID-19 Clinical Research Studies (7 papers). Wade L. Schulz is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (10 papers), Machine Learning in Healthcare (9 papers) and COVID-19 Clinical Research Studies (7 papers). Wade L. Schulz collaborates with scholars based in United States, Brazil and Spain. Wade L. Schulz's co-authors include Harlan M. Krumholz, Suveen Angraal, Thomas J S Durant, F. Perry Wilson, Adrian D. Haimovich, Leslie A. Schiff, H. P. Young, Amelia K. Haj, Richard Torres and David van Dijk and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Circulation and Nature Communications.

In The Last Decade

Wade L. Schulz

61 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Blockchain Technology

2017 256 citations Harlan M. Krumholz, Wade L. Schulz et al. Circulation Cardiovascular Quality and Outcomes profile →
Effectiveness of the CoronaVac vaccine in older adults during a gamma variant associated epidemic of covid-19 in Brazil: test negative case-control study

2021 195 citations Otávio T. Ranzani, Matt D. T. Hitchings et al. profile →
Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation

2020 176 citations Adrian D. Haimovich, H. P. Young et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wade L. Schulz United States	20	543	251	239	233	192	69	1.7k
Hamish Fraser United States	30	614 1.1×	134 0.5×	639 2.7×	389 1.7×	293 1.5×	104	3.2k
Richard Conway Ireland	29	155 0.3×	180 0.7×	468 2.0×	159 0.7×	148 0.8×	189	3.2k
Karthik Natarajan United States	19	261 0.5×	82 0.3×	319 1.3×	58 0.2×	284 1.5×	74	1.5k
Allison B. McCoy United States	24	137 0.3×	100 0.4×	179 0.7×	87 0.4×	365 1.9×	98	2.0k
Fei Tian China	13	1.7k 3.1×	85 0.3×	307 1.3×	95 0.4×	87 0.5×	38	3.6k
Daniel R. Masys United States	28	335 0.6×	191 0.8×	263 1.1×	67 0.3×	284 1.5×	75	3.0k
Vasa Ćurčin United Kingdom	29	86 0.2×	211 0.8×	591 2.5×	200 0.9×	496 2.6×	140	3.0k
Burke W. Mamlin United States	22	226 0.4×	41 0.2×	217 0.9×	201 0.9×	190 1.0×	53	1.7k
William J. Long United States	31	276 0.5×	196 0.8×	232 1.0×	108 0.5×	669 3.5×	215	3.6k
Vida Abedi United States	25	205 0.4×	190 0.8×	617 2.6×	20 0.1×	239 1.2×	111	2.3k

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-authorship network of co-authors of Wade L. Schulz

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

All Works

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20 of 20 papers shown

Dudgeon, Sarah, Nagarjuna R. Cheemarla, H. P. Young, et al.. (2025). Nasal biomarker testing to rule out viral respiratory infection and triage samples: a test performance study. EBioMedicine. 117. 105820–105820.

Cheemarla, Nagarjuna R., et al.. (2024). High burden of viruses and bacterial pathobionts drives heightened nasal innate immunity in children. The Journal of Experimental Medicine. 221(9). 7 indexed citations

Lu, Yuan, Ellen C. Keeley, Rhonda M. Cooper‐DeHoff, et al.. (2024). Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks. BMC Cardiovascular Disorders. 24(1). 497–497. 1 indexed citations

Brush, John E., Yuan Lu, Yuntian Liu, et al.. (2024). Hypertension Trends and Disparities Over 12 Years in a Large Health System: Leveraging the Electronic Health Records. Journal of the American Heart Association. 13(9). e033253–e033253. 2 indexed citations

Chaguza, Chrispin, David Ferguson, Wade L. Schulz, et al.. (2024). Genome-wide association study between SARS-CoV-2 single nucleotide polymorphisms and virus copies during infections. PLoS Computational Biology. 20(9). e1012469–e1012469. 1 indexed citations

Taylor, Richard A., Aidan Gilson, Wade L. Schulz, et al.. (2023). Computational phenotypes for patients with opioid-related disorders presenting to the emergency department. PLoS ONE. 18(9). e0291572–e0291572. 2 indexed citations

Lind, Margaret L., Murilo Dorión, Sarah Lapidus, et al.. (2023). Evidence of leaky protection following COVID-19 vaccination and SARS-CoV-2 infection in an incarcerated population. Nature Communications. 14(1). 5055–5055. 14 indexed citations

Sangha, Veer, Bobak J. Mortazavi, Adrian D. Haimovich, et al.. (2022). Automated multilabel diagnosis on electrocardiographic images and signals. Nature Communications. 13(1). 1583–1583. 64 indexed citations

Lind, Margaret L., Alexander J. Robertson, Julio Silva, et al.. (2022). Association between primary or booster COVID-19 mRNA vaccination and Omicron lineage BA.1 SARS-CoV-2 infection in people with a prior SARS-CoV-2 infection: A test-negative case–control analysis. PLoS Medicine. 19(12). e1004136–e1004136. 11 indexed citations

10.

Khera, Rohan, Bobak J. Mortazavi, Veer Sangha, et al.. (2022). A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations. npj Digital Medicine. 5(1). 27–27. 8 indexed citations

11.

Durant, Thomas J S, Sarah Dudgeon, Nathan D. Price, et al.. (2021). Applications of Digital Microscopy and Densely Connected Convolutional Neural Networks for Automated Quantification of Babesia-Infected Erythrocytes. Clinical Chemistry. 68(1). 218–229. 13 indexed citations

12.

Schulz, Wade L., H. P. Young, Andreas Coppi, et al.. (2021). Temporal relationship of computed and structured diagnoses in electronic health record data. BMC Medical Informatics and Decision Making. 21(1). 61–61. 11 indexed citations

13.

Mori, Makoto, Thomas J S Durant, Chenxi Huang, et al.. (2021). Toward Dynamic Risk Prediction of Outcomes After Coronary Artery Bypass Graft: Improving Risk Prediction With Intraoperative Events Using Gradient Boosting. Circulation Cardiovascular Quality and Outcomes. 14(6). e007363–e007363. 13 indexed citations

14.

Hitchings, Matt D. T., Otávio T. Ranzani, Murilo Dorión, et al.. (2021). Effectiveness of ChAdOx1 vaccine in older adults during SARS-CoV-2 Gamma variant circulation in São Paulo. Nature Communications. 12(1). 6220–6220. 52 indexed citations

15.

Lu, Yuan, Chenxi Huang, Shiwani Mahajan, et al.. (2020). Leveraging the Electronic Health Records for Population Health: A Case Study of Patients With Markedly Elevated Blood Pressure. Journal of the American Heart Association. 9(7). e015033–e015033. 13 indexed citations

16.

Caraballo, César, Rohan Khera, Philip G. Jones, et al.. (2020). Rates and Predictors of Patient Underreporting of Hospitalizations During Follow-Up After Acute Myocardial Infarction. Circulation Cardiovascular Quality and Outcomes. 13(7). e006231–e006231. 7 indexed citations

17.

Schulz, Wade L., et al.. (2020). Agile Health Care Analytics: Enabling Real-Time Disease Surveillance With a Computational Health Platform. Journal of Medical Internet Research. 22(5). e18707–e18707. 15 indexed citations

18.

Durant, Thomas J S, Dustin R. Bunch, Andreas Coppi, et al.. (2019). Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform. Journal of Medical Internet Research. 21(4). e13043–e13043. 50 indexed citations

19.

Huang, Chenxi, Karthik Murugiah, Shiwani Mahajan, et al.. (2018). Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLoS Medicine. 15(11). e1002703–e1002703. 92 indexed citations

20.

Schulz, Wade L.. (1989). Rabies in cattle, epidemiology and clinical features.. 44(6). 201–204.

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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