Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.

ABSTRACT:

Background

Machine-learning classifiers mostly offer good predictive performance and are increasingly used to support shared decision-making in clinical practice. Focusing on performance and practicability, this study evaluates prediction of patient-reported outcomes (PROs) by eight supervised classifiers including a linear model, following hip and knee replacement surgery.

Methods

NHS PRO data (130,945 observations) from April 2015 to April 2017 were used to train and test eight classifiers to predict binary postoperative improvement based on minimal important differences. Area under the receiver operating characteristic, J-statistic and several other metrics were calculated. The dependent outcomes were generic and disease-specific improvement based on the EQ-5D-3L visual a

SUBMITTER: Huber M 

PROVIDER: S-EPMC6325823 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature