Using Machine Learning to Predict Adherence to Recommended Imaging Follow-Up
Author:
Funder
National Cancer Institute
Association of University Radiologists
Publisher
Elsevier BV
Reference5 articles.
1. Socioeconomic factors and clinical context can predict adherence to incidental pulmonary nodule follow-up via machine learning models;Wang;J Am Coll Radiol,2024
2. BI-RADS-0 screening mammography: risk factors that prevent or delay follow-up time to diagnostic evaluation;Platt;J Am Coll Radiol,2022
3. Factors affecting adherence to recommendations for additional imaging of incidental findings in radiology reports;Hansra;J Am Coll Radiol,2021
4. Patient adherence to screening for lung cancer in the US: a systematic review and meta-analysis;Lopez-Olivo;JAMA Netw Open,2020
5. Multilevel predictors of continued adherence to breast cancer screening among women ages 50-74 years in a screening population;Beaber;J Womens Health (Larchmt),2019
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