This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (40) Big Data (297) Machine Learning (1856) Precision Medicine and Genetics (68) Cancer Epidemiology, Cancer Surveillance and Infodemiology (98) Innovations in Cancer Diagnostic and Decision Support (89) Lung Cancer (51) Secondary Use of Clinical Data for Research and Surveillance (439)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64399, first published .
Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

Authors of this article:

Alan James Michael Brnabic1 Author Orcid Image ;   Ilya Lipkovich2 Author Orcid Image ;   Zbigniew Kadziola2 Author Orcid Image ;   Dan He3 Author Orcid Image ;   Peter M Krein2 Author Orcid Image ;   Lisa M Hess2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Alan James Michael Brnabic   1 , MS ;   Ilya Lipkovich   2 , PhD ;   Zbigniew Kadziola   2 , MS ;   Dan He   3 , MS ;   Peter M Krein   2 , PhD ;   Lisa M Hess   2 , PhD

1 Eli Lilly and Company, Sydney, Australia

2 Eli Lilly and Company, Indianapolis, IN, United States

3 Syneos Health, Morrisville, NC, United States

Corresponding Author: