Góngora Alonso, Susel and Herrera Montano, Isabel and Martín Ayala, Juan Luis and Rodrigues, Joel J. P. C. and Franco-Martín, Manuel and de la Torre Díez, Isabel UNSPECIFIED, UNSPECIFIED, juan.martin@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2023) Machine Learning Models to Predict Readmission Risk of Patients with Schizophrenia in a Spanish Region. International Journal of Mental Health and Addiction. ISSN 1557-1874
Full text not available from this repository.Abstract
Currently, high hospital readmission rates have become a problem for mental health services, because it is directly associated with the quality of patient care. The development of predictive models with machine learning algorithms allows the assessment of readmission risk in hospitals. The main objective of this paper is to predict the readmission risk of patients with schizophrenia in a region of Spain, using machine learning algorithms. In this study, we used a dataset with 6089 electronic admission records corresponding to 3065 patients with schizophrenia disorders. Data were collected in the period 2005–2015 from acute units of 11 public hospitals in a Spain region. The Random Forest classifier obtained the best results in predicting the readmission risk, in the metrics accuracy = 0.817, recall = 0.887, F1-score = 0.877, and AUC = 0.879. This paper shows the algorithm with highest accuracy value and determines the factors associated with readmission risk of patients with schizophrenia in this population. It also shows that the development of predictive models with a machine learning approach can help improve patient care quality and develop preventive treatments.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Algorithms; Machine learning; Readmission; Risk factors; Schizophrenia |
| Subjects: | Subjects > Psychology |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 09 Feb 2023 15:11 |
| Last Modified: | 21 Oct 2024 12:10 |
| URI: | http://repositorio.funiber.org/id/eprint/5792 |
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