Predicting falls-related admissions in older adults in Alberta, Canada: a machine-learning falls prevention tool developed using population administrative health data.

ABSTRACT:

Objective

To construct a machine-learning (ML) model for health systems with organised falls prevention programmes to identify older adults at risk for fall-related admissions.

Design

This prognostic study used population-level administrative health data to develop an ML prediction model.

Setting

This study took place in Alberta, Canada during 2018-2019.

Participants

Albertans aged 65 and older with at least one prior admission. Those with palliative conditions or emigrated out of Alberta were excluded.

Exposure

Unit of analysis was the individual person.

Main outcomes/measures

We identified fall-related admissions. A CatBoost model was developed on 2018 data to predict risk of fall-related emergency department visits or hospitalisations. Temporal

SUBMITTER: Sharma V 

PROVIDER: S-EPMC10445355 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature