Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study.

ABSTRACT:

Objective

To predict older adults' risk of avoidable hospitalisation related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada.

Design, setting and participants

A retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years (2008-2017). The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period.

Data sources

Administrative health data from Ontario, Canada obtained from the (ICES formely known as the Institute for Clinical Evaluative Sciences Data Repository.

Main outcome measures

Risk of hospitalisations due to ACSCs 1 year after t

SUBMITTER: Yi SE 

PROVIDER: S-EPMC8977821 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature