Nathan C. Hurley

922 total citations · 2 hit papers
13 papers, 477 citations indexed

About

Nathan C. Hurley is a scholar working on Artificial Intelligence, Surgery and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Nathan C. Hurley has authored 13 papers receiving a total of 477 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Surgery and 4 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Nathan C. Hurley's work include Machine Learning in Healthcare (5 papers), Artificial Intelligence in Healthcare and Education (4 papers) and Topic Modeling (2 papers). Nathan C. Hurley is often cited by papers focused on Machine Learning in Healthcare (5 papers), Artificial Intelligence in Healthcare and Education (4 papers) and Topic Modeling (2 papers). Nathan C. Hurley collaborates with scholars based in United States and Argentina. Nathan C. Hurley's co-authors include Bobak J. Mortazavi, Harlan M. Krumholz, Nihar R. Desai, Frederick A. Masoudi, Alyssa Berkowitz, Joseph S. Ross, Jeptha P. Curtis, John C. Messenger, Amit P. Amin and Sanket S. Dhruva and has published in prestigious journals such as JAMA, Transfusion and JAMA Network Open.

In The Last Decade

Nathan C. Hurley

11 papers receiving 472 citations

Hit Papers

Association of Use of an Intravascular Microaxial Left Ve... 2020 2026 2022 2024 2020 2021 50 100 150 200
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.

  1. Association of Use of an Intravascular Microaxial Left Ventricular Assist Device vs Intra-aortic Balloon Pump With In-Hospital Mortality and Major Bleeding Among Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock
    2020 220 citations Sanket S. Dhruva, Joseph S. Ross et al. JAMA profile →
  2. Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction
    2021 155 citations Rohan Khera, Julian S. Haimovich et al. JAMA Cardiology profile →

Peers

Nathan C. Hurley
Elric Zweck Germany
Dorit Knappe Germany
Eilon Gabel United States
Daniel Fudulu United Kingdom
Alexander Bonde Denmark
Alina Zubarevich Germany
Darshan H. Brahmbhatt Canada
Anjan Tibrewala United States
Julian S. Haimovich United States
Alyssa Berkowitz United States
Elric Zweck Germany
Nathan C. Hurley
Citations per year, relative to Nathan C. Hurley Nathan C. Hurley (= 1×) peers Elric Zweck

Countries citing papers authored by Nathan C. Hurley

Since Specialization
Citations

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

Fields of papers citing papers by Nathan C. Hurley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathan C. Hurley. 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 Nathan C. Hurley. The network helps show where Nathan C. Hurley may publish in the future.

Co-authorship network of co-authors of Nathan C. Hurley

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

All Works

13 of 13 papers shown
1.
Hurley, Nathan C., Sanket S. Dhruva, Rohan Khera, et al.. (2025). Towards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention. PLOS Digital Health. 4(6). e0000906–e0000906.
2.
Hurley, Nathan C. & Eric S. Schwenk. (2024). Can artificial intelligence make clinical decisions in regional anesthesia? An infographic. Regional Anesthesia & Pain Medicine. 49(9). 668–668.
3.
Hurley, Nathan C., Rajnish K. Gupta, Kristopher M. Schroeder, & Aaron S. Hess. (2024). Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?. Regional Anesthesia & Pain Medicine. 49(9). 661–667. 5 indexed citations
4.
Hurley, Nathan C., Sanket S. Dhruva, Nihar R. Desai, et al.. (2023). Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation. PubMed. 4(4). 1–18. 1 indexed citations
5.
Hurley, Nathan C., Kristopher M. Schroeder, & Aaron S. Hess. (2023). Would doctors dream of electric blood bankers? Large language model‐based artificial intelligence performs well in many aspects of transfusion medicine. Transfusion. 63(10). 1833–1840. 13 indexed citations
6.
Hurley, Nathan C., Adrian D. Haimovich, Richard A. Taylor, & Bobak J. Mortazavi. (2022). Visualization of emergency department clinical data for interpretable patient phenotyping. Smart Health. 25. 100285–100285. 3 indexed citations
7.
Dhruva, Sanket S., Joseph S. Ross, Bobak J. Mortazavi, et al.. (2021). Use of Mechanical Circulatory Support Devices Among Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock. JAMA Network Open. 4(2). e2037748–e2037748. 64 indexed citations
8.
Hurley, Nathan C., Alyssa Berkowitz, Frederick A. Masoudi, et al.. (2021). Outcomes-Driven Clinical Phenotyping in Cardiogenic Shock using a Mixture of Experts. 31. 1–4. 1 indexed citations
9.
Khera, Rohan, Julian S. Haimovich, Nathan C. Hurley, et al.. (2021). Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction. JAMA Cardiology. 6(6). 633–633. 155 indexed citations breakdown →
10.
Hurley, Nathan C., Erica S. Spatz, Harlan M. Krumholz, Roozbeh Jafari, & Bobak J. Mortazavi. (2020). A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders. PubMed Central. 2(1). 1–42. 6 indexed citations
11.
Dhruva, Sanket S., Joseph S. Ross, Bobak J. Mortazavi, et al.. (2020). Association of Use of an Intravascular Microaxial Left Ventricular Assist Device vs Intra-aortic Balloon Pump With In-Hospital Mortality and Major Bleeding Among Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock. JAMA. 323(8). 734–734. 220 indexed citations breakdown →
12.
Hurley, Nathan C., et al.. (2019). Sparse Embedding for Interpretable Hospital Admission Prediction. PubMed. 2019. 3438–3441. 4 indexed citations
13.
Hurley, Nathan C., et al.. (2013). Indium(III) triflate — a catalyst for greener aromatic alkylation reactions. Canadian Journal of Chemistry. 91(12). 1262–1265. 5 indexed citations

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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