Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

ABSTRACT: OBJECTIVE:Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods. METHODS:We devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to asse

SUBMITTER: Engels A 

PROVIDER: S-EPMC7237034 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature