Meeting
2020 ASCO Quality Care Symposium
Machine learning to predict tamoxifen adherence among U.S. commercially insured breast cancer patients.
Authors
Tyler J. O'Neill
Roche Diagnostics Information Solutions, Pleasanton, CA
Tyler J. O'Neill, Vishakha Sharma, Athanasios Siadimas, Amir Babaeian, Gayathri Yerrapragada
Disclaimer
This material on this page is ©2024 American Society of Clinical Oncology, all rights reserved. Licensing available upon request. For more information, please contact licensing@asco.org
Poster Details
Session Type
Poster Session
Session Title
On-Demand Poster Session: Technology and Innovation in Quality of Care
Track
Technology and Innovation in Quality of Care
Sub Track
Real-World Evidence
Citation
J Clin Oncol 38, 2020 (suppl 29; abstr 276)
DOI
10.1200/JCO.2020.38.29_suppl.276
Abstract #
276
Poster Bd #
Online Only
Abstract Disclosures
Similar Posters
Poster
2023 ASCO Quality Care Symposium
Defining and evaluating potentially preventable ED visits in oncology patients including using early machine learning models.
First Author: Sonal Gandhi
Poster
2023 ASCO Annual Meeting
Real-world outcomes with adjuvant nonsteroidal aromatase inhibitors (NSAIs) vs tamoxifen (TAM) in patients with hormone receptor−positive/human epidermal growth factor receptor 2−negative (HR+/HER2−) early breast cancer (EBC): A US database analysis.
First Author: Stephanie L. Graff
Poster
2023 ASCO Annual Meeting
Influence of adherence to adjuvant endocrine therapy in early breast cancer: Results from a large German real-world claims data analysis.
First Author: Dominik Dannehl
Poster
2024 ASCO Breakthrough
Exploring depression risk among breast cancer survivors: A nationwide cohort study in South Korea.
First Author: Hea Lim Choi