Predictive modeling for identification of older adults with high utilization of health and social services.

ABSTRACT:

Aim

Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aims to utilize machine learning models to detect high-risk groups of excessive health and social services utilization at early stage, facilitating the implementation of preventive interventions.

Methods

We used pseudonymized data covering a four-year period and including information on a total of 33,374 senior citizens from Southern Finland. The endpoint was defined based on the occurrence of unplanned healthcare visits and the total number of different services used. Input features included individual's basic demographics, health s

SUBMITTER: Sourkatti H 

PROVIDER: S-EPMC11552250 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature