Actionable Predictive Factors of Homelessness in a Psychiatric Population: Results from the REHABase Cohort Using a Machine Learning Approach.

ABSTRACT:

Background

There is a lack of knowledge regarding the actionable key predictive factors of homelessness in psychiatric populations. Therefore, we used a machine learning model to explore the REHABase database (for rehabilitation database-n = 3416), which is a cohort of users referred to French psychosocial rehabilitation centers in France.

Methods

First, we analyzed whether the different risk factors previously associated with homelessness in mental health were also significant risk factors in the REHABase. In the second step, we used unbiased classification and regression trees to determine the key predictors of homelessness. Post hoc analyses were performed to examine the importance of the predictors and to explore the impact of cognitive factors among the participa

SUBMITTER: Lio G 

PROVIDER: S-EPMC9565981 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature