Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture.

ABSTRACT:

Background

Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture.

Methods

This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into

SUBMITTER: Kitcharanant N 

PROVIDER: S-EPMC9131628 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature