{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:16:03Z","timestamp":1760145363508,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Project Horizon 2020 SC1-BHC-02-2019","award":["848098"],"award-info":[{"award-number":["848098"]}]},{"name":"Plataforma Andaluza de Bioinfo\u2014rm\u00e1tica at the University of M\u00e1laga","award":["848098"],"award-info":[{"award-number":["848098"]}]},{"name":"the supercomputing infrastructure of the NLHPC (ECM-02, Powered@NLHPC)","award":["848098"],"award-info":[{"award-number":["848098"]}]},{"name":"the Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT)","award":["848098"],"award-info":[{"award-number":["848098"]}]},{"name":"European Regional Development Fund (ERDF)","award":["848098"],"award-info":[{"award-number":["848098"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Nowadays, most of the health expenditure is due to chronic patients who are readmitted several times for their pathologies. Personalized prevention strategies could be developed to improve the management of these patients. The aim of the present work was to develop local predictive models using interpretable machine learning techniques to early identify individual unscheduled hospital readmissions. To do this, a retrospective, case-control study, based on information regarding patient readmission in 2018\u20132019, was conducted. After curation of the initial dataset (n = 76,210), the final number of participants was n = 29,026. A machine learning analysis was performed following several algorithms using unscheduled hospital readmissions as dependent variable. Local model-agnostic interpretability methods were also performed. We observed a 13% rate of unscheduled hospital readmissions cases. There were statistically significant differences regarding age and days of stay (p &lt; 0.001 in both cases). A logistic regression model revealed chronic therapy (odds ratio: 3.75), diabetes mellitus history (odds ratio: 1.14), and days of stay (odds ratio: 1.02) as relevant factors. Machine learning algorithms yielded better results regarding sensitivity and other metrics. Following, this procedure, days of stay and age were the most important factors to predict unscheduled hospital readmissions. Interestingly, other variables like allergies and adverse drug reaction antecedents were relevant. Individualized prediction models also revealed a high sensitivity. In conclusion, our study identified significant factors influencing unscheduled hospital readmissions, emphasizing the impact of age and length of stay. We introduced a personalized risk model for predicting hospital readmissions with notable accuracy. Future research should include more clinical variables to refine this model further.<\/jats:p>","DOI":"10.3390\/make6030080","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T16:51:12Z","timestamp":1721321472000},"page":"1653-1666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Global and Local Interpretable Machine Learning Allow Early Prediction of Unscheduled Hospital Readmission"],"prefix":"10.3390","volume":"6","author":[{"given":"Rafael","family":"Ruiz de San Mart\u00edn","sequence":"first","affiliation":[{"name":"Servicio Murciano de Salud, Hospital Universitario Virgen de la Arrixaca, Crta. El Palmar, 30120 Murcia, Spain"}]},{"given":"Catalina","family":"Morales-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Faculty of Nursing, Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}]},{"given":"Carmen","family":"Barber\u00e1","sequence":"additional","affiliation":[{"name":"Faculty of Nursing, Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}]},{"given":"Carlos","family":"Mart\u00ednez-Cort\u00e9s","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics and High Performance Computing (BIO-HPC), Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1158-8877","authenticated-orcid":false,"given":"Antonio Jes\u00fas","family":"Banegas-Luna","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics and High Performance Computing (BIO-HPC), Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-0192","authenticated-orcid":false,"given":"Francisco Jos\u00e9","family":"Segura-M\u00e9ndez","sequence":"additional","affiliation":[{"name":"Hydrological Modeling and Research Lab, Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4468-7898","authenticated-orcid":false,"given":"Horacio","family":"P\u00e9rez-S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Structural Bioinformatics and High Performance Computing (BIO-HPC), Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}]},{"given":"Isabel","family":"Morales-Moreno","sequence":"additional","affiliation":[{"name":"Faculty of Nursing, Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9255-4630","authenticated-orcid":false,"given":"Juan Jos\u00e9","family":"Hern\u00e1ndez-Morante","sequence":"additional","affiliation":[{"name":"Faculty of Nursing, Universidad Cat\u00f3lica de Murcia (UCAM), Avd. de los Jer\u00f3nimos, 30107 Murcia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"ref_1","first-page":"422","article-title":"Higher Patient Complexities Are Associated with Increased Length of Stay, Complications, and Readmissions After Total Hip Arthroplasty","volume":"38","author":"Guntaka","year":"2021","journal-title":"Surg. 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