Author

Nate Hurley

Texas A&M University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Assay:electronic health records· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Bobak J. MortazaviiD· 1 shared Chenxi HuangiD· 1 shared Frederick A. MasoudiiD· 1 shared Harlan M. KrumholziD· 1 shared John A. SpertusiD· 1 shared Julian S. HaimovichiD· 1 shared Nihar R. DesaiiD· 1 shared Robert L. McNamaraiD· 1 shared

Activity by Year

2019-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Bobak J. MortazaviORCID

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1 paper

Recent·Most cited

Machine-learning to Improve Prediction of Mortality following Acute Myocardial Infarction: An Assessment in the NCDR-Chest Pain-Myocardial Infarction Registry

Rohan Khera, Julian S. Haimovich, Nate Hurley, Robert L. McNamara, John A. Spertus, Nihar R. Desai, Frederick A. Masoudi, Chenxi Huang, Sharon‐Lise T. Normand, Bobak J. Mortazavi, Harlan M. Krumholz

This study develops and validates machine learning models to predict in-hospital mortality after acute myocardial infarction, finding improved performance compared to logistic regression.

bioRxiv · Feb 5, 2019 · PreprintBioinformatics