{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T04:19:29Z","timestamp":1778127569927,"version":"3.51.4"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005278","name":"University of Antioquia","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005278","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. This cohort study evaluated three machine learning algorithms\u2014XGBoost, Elastic Net logistic regression, and an Artificial Neural Network\u2014to develop a prediction model for three outcomes: mortality, hospitalization, and emergency department visits. The objective was to build a clinical decision support system for patients with noncommunicable diseases treated at the Alma Mater Hospital complex in Medell\u00edn, Colombia. We collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71.83 years, with 63.8% women and 29.7% receiving home care. The most prevalent medical conditions were diabetes (52.9%), hypertension (67.2%), dyslipidemia (57.3%), and COPD (19.4%). For mortality prediction, the Elastic Net logistic regression model achieved an AUCROC of 0.883 (95% CI: 0.848\u20130.917), the XGBoost model reached an AUCROC of 0.896 (95% CI: 0.865\u20130.927), and the Neural Network achieved 0.886 (95% CI: 0.853\u20130.916). For hospitalization, the Elastic Net model had an AUCROC of 0.952 (95% CI: 0.937\u20130.965), the XGBoost model achieved 0.963 (95% CI: 0.952\u20130.974), and the Neural Network scored 0.932 (95% CI: 0.915\u20130.948). For emergency department visits, the AUCROC values were 0.980 (95% CI: 0.971\u20130.987) for Elastic Net, 0.977 (95% CI: 0.967\u20130.986) for XGBoost, and 0.976 (95% CI: 0.968\u20130.982) for the neural network. A dashboard was developed to interact with an ensemble risk categorization segmenting patient risk in the cohort to aid in clinical decision-making. A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with Noncommunicable Diseases for effective decision-making.\u00a0<\/jats:p>","DOI":"10.1007\/s10916-025-02140-z","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T19:21:01Z","timestamp":1738610461000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases"],"prefix":"10.1007","volume":"49","author":[{"given":"Alejandro","family":"Hern\u00e1ndez-Arango","sequence":"first","affiliation":[]},{"given":"Mar\u00eda Isabel","family":"Arias","sequence":"additional","affiliation":[]},{"given":"Viviana","family":"P\u00e9rez","sequence":"additional","affiliation":[]},{"given":"Luis Daniel","family":"Chavarr\u00eda","sequence":"additional","affiliation":[]},{"given":"Fabian","family":"Jaimes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"issue":"1","key":"2140_CR1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.12804\/revsalud14.01.2016.09","volume":"14","author":"K Gallardo-Solarte","year":"2016","unstructured":"K. Gallardo-Solarte K., F. P. Benavides-Acosta F.P., and R. Rosales-Jim\u00e9nez R., \u201cCostos de la enfermedad cr\u00f3nica no transmisible: la realidad colombiana,\u201d Rev. Cienc. Salud, vol. 14, no. 1, pp. 103\u2013114, Feb. 2016, doi: https:\/\/doi.org\/10.12804\/revsalud14.01.2016.09.","journal-title":"Rev. Cienc. Salud"},{"key":"2140_CR2","doi-asserted-by":"publisher","unstructured":"J. F. Orueta Mendia, A. Garc\u00eda-\u00c1lvarez, E. Alonso-Mor\u00e1n, and R. Nu\u00f1o-Solinis, \u201cDesarrollo de un modelo de predicci\u00f3n de riesgo de hospitalizaciones no programadas en el Pa\u00eds Vasco,\u201d Rev. Esp. Salud Publica, vol. 88, no. 2, pp. 251\u2013260, Apr. 2014, doi: https:\/\/doi.org\/10.4321\/s1135-57272014000200007.","DOI":"10.4321\/s1135-57272014000200007"},{"issue":"2","key":"2140_CR3","doi-asserted-by":"publisher","first-page":"171","DOI":"10.11144\/javeriana.umed57-2.grde","volume":"57","author":"I Gorbanev","year":"2016","unstructured":"I. Gorbanev, A. E. Cort\u00e9s Mart\u00ednez, S. Agudelo Londo\u00f1o, and F. J. Yepes Lujan, \u201cGrupos relacionados por el diagn\u00f3stico: experiencia en tres hospitales de alta complejidad en Colombia,\u201d Univ. M\u00e9dica, vol. 57, no. 2, pp. 171\u2013181, Jul. 2016, doi: https:\/\/doi.org\/10.11144\/javeriana.umed57-2.grde.","journal-title":"Univ. M\u00e9dica"},{"key":"2140_CR4","unstructured":"E. Nolte, World Health Organization: Regional Office for Europe, and C. Knai, Assessing chronic disease management in European health systems. Europe, UK: WHO Regional Office for Europe, 2015."},{"issue":"1","key":"2140_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.ogla.2020.08.006","volume":"4","author":"BC Stagg","year":"2021","unstructured":"B. C. Stagg et al., \u201cSpecial Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic,\u201d Ophthalmol Glaucoma, vol. 4, no. 1, pp. 5\u20139, Jan-Feb 2021, doi: https:\/\/doi.org\/10.1016\/j.ogla.2020.08.006.","journal-title":"Ophthalmol Glaucoma"},{"key":"2140_CR6","doi-asserted-by":"publisher","first-page":"e15","DOI":"10.26633\/rpsp.2021.15","volume":"45","author":"V Garc\u00eda-Arango","year":"2021","unstructured":"V. Garc\u00eda-Arango, J. Osorio-Ciro, D. Aguirre-Acevedo, C. Vanegas-Vargas, C. Clavijo-Usuga, and J. Gallo-Villegas, \u201cValidaci\u00f3n predictiva de un m\u00e9todo de clasificaci\u00f3n funcional en adultos mayores,\u201d Rev. Panam. Salud Publica, vol. 45, p. e15, Apr. 2021, doi: https:\/\/doi.org\/10.26633\/rpsp.2021.15.","journal-title":"Rev. Panam. 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Kim, \u201cMachine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients,\u201d Healthc. Inform. Res., vol. 25, no. 4, pp. 305\u2013312, Oct. 2019, doi: https:\/\/doi.org\/10.4258\/hir.2019.25.4.305.","DOI":"10.4258\/hir.2019.25.4.305"},{"key":"2140_CR20","doi-asserted-by":"publisher","unstructured":"R. Khera et al., \u201cUse of Machine Learning Models to Predict Death After Acute Myocardial Infarction,\u201d JAMA Cardiol, vol. 6, no. 6, pp. 633\u2013641, Jun. 2021, doi: https:\/\/doi.org\/10.1001\/jamacardio.2021.0122.","DOI":"10.1001\/jamacardio.2021.0122"},{"issue":"6","key":"2140_CR21","doi-asserted-by":"publisher","first-page":"e0252585","DOI":"10.1371\/journal.pone.0252585","volume":"16","author":"EJ MacKay","year":"2021","unstructured":"E. J. MacKay et al., \u201cApplication of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations,\u201d PLoS One, vol. 16, no. 6, p. e0252585, Jun. 2021, doi: https:\/\/doi.org\/10.1371\/journal.pone.0252585.","journal-title":"PLoS One"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02140-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02140-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02140-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T19:21:04Z","timestamp":1738610464000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02140-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,3]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2140"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02140-z","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,3]]},"assertion":[{"value":"1 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there was no funding.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Ethics Committee on Human Research at the University Research Unit (CBE-SIU), Universidad de Antioquia, under number 20-114-922. The Committee deemed the retrospective observational study ethically valid and posing no risk to participants. The Committee had ongoing access to study data while maintaining confidentiality and approved the measures for participant protection and the informed consent process. The study was conducted in accordance with Resolution 008430 of October 4, 1993, of the Colombian Ministry of Health, which established the scientific, technical, and administrative standards for health research; the principles of the World Medical Assembly as stated in the Declaration of Helsinki of 1964, last updated in 2013; the Code of Federal Regulations, Title 45, Part 46, for the protection of human subjects by the U.S. Department of Health and Human Services and the National Institutes of Health (June 18, 1991); and Resolution 2378 of 2008 of the Ministry of Social Protection of Colombia, which adopts Good Clinical Practices for institutions conducting research with medications in humans.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.\u00a0","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"19"}}