{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T09:10:08Z","timestamp":1764666608837,"version":"3.46.0"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"del Ministero delle Imprese e del Made in Italy","award":["mimit.AOO_IAI.REGISTRO INTERNO.R.0001470.08-05-2023"],"award-info":[{"award-number":["mimit.AOO_IAI.REGISTRO INTERNO.R.0001470.08-05-2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The widespread fragmentation of patient information across heterogeneous systems and the lack of standardized integration mechanisms hinder efficient and comprehensive medical diagnostics. To address these limitations, this work presents an architecture framework designed to support physicians in the diagnostic process by integrating clinical and socio-health information (patient medical histories), structured documents extracted from Health Information System (HIS), and data automatically extracted from diagnostic images using Artificial Intelligence (AI) techniques. The proposed architecture is made by several modules, in particular a Decision Support System (DSS) that enables risk assessment related to specific patient\u2019s clinical conditions. In addition, the clinical information retrieved is aggregated, standardized, and transmitted to external systems for follow up. Standardization and data interoperability are ensured through the adoption of the international HL7 Fast Healthcare Interoperability Resources (FHIR) standard, which facilitates seamless connection with HIS. An Android application has been developed to communicate with different HISs in order to: (i) retrieve information, (ii) aggregate clinical data, (iii) calculate patient risk scores using AI algorithms, (iv) display results to healthcare professionals, and (v) generate and share relevant clinical information with external systems in a standardized format. To demonstrate architecture\u2019s applicability, a case study on breast cancer diagnosis is presented. In this context, an AI-based Risk Assessment module was developed using the Breast Ultrasound Images Dataset (BUSI), which includes benign, malignant, and normal cases. Machine Learning algorithms were applied to perform the classification task. Model performance was evaluated using a 4-fold cross-validation strategy to ensure robustness and generalizability. The best results were achieved using the Multilayer Perceptron method, with a competitive F1-score of 0.97.<\/jats:p>","DOI":"10.3390\/info16121054","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T08:49:22Z","timestamp":1764665362000},"page":"1054","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Enhanced Architecture Model for Integrated and FHIR-Based Health Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9061-1187","authenticated-orcid":false,"given":"Nadia","family":"Brancati","sequence":"first","affiliation":[{"name":"Institute for High Performance Computing and Networking-National Research Council of Italy (ICAR-CNR), 80131 Napoli, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9477-3589","authenticated-orcid":false,"given":"Teresa","family":"Conte","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking-National Research Council of Italy (ICAR-CNR), 80131 Napoli, Italy"}]},{"given":"Simona","family":"De Pietro","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, University of Naples \u201cFederico II\u201d, 80138 Napoli, Italy"},{"name":"Pineta Grande Hospital, 81030 Castel Volturno, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4910-9922","authenticated-orcid":false,"given":"Martina","family":"Russo","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking-National Research Council of Italy (ICAR-CNR), 80131 Napoli, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8144-7506","authenticated-orcid":false,"given":"Mario","family":"Sicuranza","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking-National Research Council of Italy (ICAR-CNR), 80131 Napoli, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"ref_1","first-page":"1287","article-title":"Electronic Health Record Adoption and Interoperability among U.S. Hospitals","volume":"40","author":"Holmgren","year":"2021","journal-title":"Health Aff."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Barolli, L., Xhafa, F., Javaid, N., Spaho, E., and Kolici, V. 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