Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis.

ABSTRACT:

Objectives

Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.

Methods

Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models

SUBMITTER: Wu Y 

PROVIDER: S-EPMC10728980 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature