Early prediction of functional impairment at hospital discharge in patients with osteoporotic vertebral fracture: a machine learning approach.

ABSTRACT: Although conservative treatment is commonly used for osteoporotic vertebral fracture (OVF), some patients experience functional disability following OVF. This study aimed to develop prediction models for new-onset functional impairment following admission for OVF using machine learning approaches and compare their performance. Our study consisted of patients aged 65 years or older admitted for OVF using a large hospital-based database between April 2014 and December 2021. As the primary outcome, we defined new-onset functional impairment as a Barthel Index ≤ 60 at discharge. In the training dataset, we developed three machine learning models (random forest [RF], gradient-boosting decision tree [GBDT], and deep neural network [DNN]) and one conventional model (logistic regression [LR]). In

SUBMITTER: Masuda S 

PROVIDER: S-EPMC11682089 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature