Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)

Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001)

Background: Women undergoing cancer-related mastectomy and reconstruction are facing multiple treatment choices where post-surgical satisfaction with breasts is a key outcome. We developed and validated machine learning algorithms to predict patient-reported satisfaction with breasts at 2-year follo...

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Bibliographic Details
Main Authors: André Pfob, Babak J. Mehrara, Jonas A. Nelson, Edwin G. Wilkins, Andrea L. Pusic, Chris Sidey-Gibbons
Format: Article
Language:English
Published: Elsevier 2021-12-01
Series:Breast
Subjects:
Decision-making
Breast reconstruction
Machine learning
Individualized treatment
Breast surgery
INSPiRED
Online Access:http://www.sciencedirect.com/science/article/pii/S0960977621004665

Internet

http://www.sciencedirect.com/science/article/pii/S0960977621004665

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