000156645 001__ 156645
000156645 005__ 20251017144551.0
000156645 0247_ $$2doi$$a10.3390/jcm14103434
000156645 0248_ $$2sideral$$a143946
000156645 037__ $$aART-2025-143946
000156645 041__ $$aeng
000156645 100__ $$0(orcid)0000-0002-6518-749X$$aIoakeim-Skoufa, Ignatios
000156645 245__ $$aElectronic Health Records: A Gateway to AI-Driven Multimorbidity Solutions—A Comprehensive Systematic Review
000156645 260__ $$c2025
000156645 5060_ $$aAccess copy available to the general public$$fUnrestricted
000156645 5203_ $$aBackground/Objectives: Artificial intelligence (AI) plays an important role in real-world health research. It can address the complexities of chronic diseases and their associated negative outcomes. This systematic review aims to identify the applications of AI that utilize real-world health data for populations with multiple chronic conditions. Methods: A systematic search was performed in MEDLINE and EMBASE following PRISMA guidelines. Studies were included if they applied AI methods using data from electronic health records for patients with multimorbidity. Results: Forty-four studies met the inclusion criteria. The review revealed AI applications identifying disease clusters, predicting comorbidities, and estimating health outcomes such as mortality, adverse drug reactions, and hospital readmissions. Commonly used AI techniques included clustering methods, XGBoost, random forest, and neural networks. These methods helped identify risk factors, predict disease progression, and optimize treatment plans. Conclusions: This study emphasizes the increasing role of AI in understanding and managing multimorbidity. Integrating AI into healthcare systems can enhance resource allocation, improve care delivery efficiency, and support personalized treatment strategies. However, further research is needed to overcome existing limitations, particularly the lack of standardized performance metrics, which affects model comparability. Future research should adhere to commonly recommended evaluation practices to improve reproducibility and meta-analysis.
000156645 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/B01-23R$$9info:eu-repo/grantAgreement/ES/ISCIII/RD24-0005-0013$$9info:eu-repo/grantAgreement/ES/ISCIII-RICAPPS/RD21-0016-0019
000156645 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000156645 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000156645 700__ $$aCebollada-Herrera, Celeste
000156645 700__ $$aMarín-Bárcena, Concepción
000156645 700__ $$aRoque, Vitor
000156645 700__ $$aRoque, Fátima
000156645 700__ $$aAtkins, Kerry
000156645 700__ $$aHernández-Rodríguez, Miguel Ángel
000156645 700__ $$aAza-Pascual-Salcedo, Mercedes
000156645 700__ $$0(orcid)0000-0001-8064-8138$$aFanlo-Villacampa, Ana$$uUniversidad de Zaragoza
000156645 700__ $$aCoelho, Helena
000156645 700__ $$aLasala-Aza, Carmen
000156645 700__ $$aLedesma-Calvo, Rubén
000156645 700__ $$0(orcid)0000-0002-5440-1710$$aGimeno-Miguel, Antonio
000156645 700__ $$0(orcid)0000-0003-4629-6743$$aVicente-Romero, Jorge$$uUniversidad de Zaragoza
000156645 7102_ $$11012$$2315$$aUniversidad de Zaragoza$$bDpto. Farmac.Fisiol.y Med.L.F.$$cÁrea Farmacología
000156645 773__ $$g14, 10 (2025), 3434 [23 pp.]$$pJ. clin.med.$$tJournal of Clinical Medicine$$x2077-0383
000156645 8564_ $$s893373$$uhttps://zaguan.unizar.es/record/156645/files/texto_completo.pdf$$yVersión publicada
000156645 8564_ $$s2785251$$uhttps://zaguan.unizar.es/record/156645/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000156645 909CO $$ooai:zaguan.unizar.es:156645$$particulos$$pdriver
000156645 951__ $$a2025-10-17-14:11:50
000156645 980__ $$aARTICLE