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

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 psyc...

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Detalles Bibliográficos
Autores principales: Lio, Guillaume, Ghazzai, Malek, Haesebaert, Frédéric, Dubreucq, Julien, Verdoux, Hélène, Quiles, Clélia, Jaafari, Nemat, Chéreau-Boudet, Isabelle, Legros-Lafarge, Emilie, Guillard-Bouhet, Nathalie, Massoubre, Catherine, Gouache, Benjamin, Plasse, Julien, Barbalat, Guillaume, Franck, Nicolas, Demily, Caroline
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565981/
https://www.ncbi.nlm.nih.gov/pubmed/36231571
http://dx.doi.org/10.3390/ijerph191912268

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9565981/
https://www.ncbi.nlm.nih.gov/pubmed/36231571
http://dx.doi.org/10.3390/ijerph191912268

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