eprintid: 5792 rev_number: 6 eprint_status: archive userid: 2 dir: disk0/00/00/57/92 datestamp: 2023-02-09 23:30:07 lastmod: 2023-02-09 23:30:08 status_changed: 2023-02-09 23:30:07 type: article metadata_visibility: show creators_name: Góngora Alonso, Susel creators_name: Herrera Montano, Isabel creators_name: Martín Ayala, Juan Luis creators_name: Rodrigues, Joel J. P. C. creators_name: Franco-Martín, Manuel creators_name: de la Torre Díez, Isabel creators_id: creators_id: creators_id: juan.martin@uneatlantico.es creators_id: creators_id: creators_id: title: Machine Learning Models to Predict Readmission Risk of Patients with Schizophrenia in a Spanish Region ispublished: pub subjects: uneat_ps divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica full_text_status: none keywords: Algorithms; Machine learning; Readmission; Risk factors; Schizophrenia 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. date: 2023 publication: International Journal of Mental Health and Addiction id_number: doi:10.1007/s11469-022-01001-x refereed: TRUE issn: 1557-1874 official_url: http://doi.org/10.1007/s11469-022-01001-x access: close language: en citation: Artículo Materias > Psicología Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Cerrado Inglés 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. metadata Góngora Alonso, Susel; Herrera Montano, Isabel; Martín Ayala, Juan Luis; Rodrigues, Joel J. P. C.; Franco-Martín, Manuel y de la Torre Díez, Isabel mail SIN ESPECIFICAR, SIN ESPECIFICAR, juan.martin@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (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