Open AccessDevelopment and Internal Validation of Machine Learning Algorithms for Preoperative Survival Prediction of Extremity Metastatic Disease
Author(s) -
Quirina C. B. S. Thio,
Aditya V. Karhade,
Paul T. Ogink,
Jos A. M. Bramer,
Marco Ferrone,
Santiago Lozano Calderón,
Kevin A. Raskin,
Joseph H. Schwab
Publication year - 2019
Publication title -
clinical orthopaedics and related research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.178
H-Index - 204
eISSN - 1528-1132
pISSN - 0009-921X
DOI - 10.1097/corr.0000000000000997
Subject(s) - medicine , interquartile range , algorithm , humerus , intramedullary rod , machine learning , surgery , retrospective cohort study , cohort , radiology , computer science
A preoperative estimation of survival is critical for deciding on the operative management of metastatic bone disease of the extremities. Several tools have been developed for this purpose, but there is room for improvement. Machine learning is an increasingly popular and flexible method of prediction model building based on a data set. It raises some skepticism, however, because of the complex structure of these models.