From development to clinical practice: deployment of an interoperable and secure ML-based CDSS to aid in the early detection of sepsis

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dc.contributor.authorSerrano García, Ana
dc.contributor.authorLópez, David
dc.contributor.authorMacias-Fassio, Eric
dc.contributor.authorSalas-Sosa, Santiago
dc.contributor.authorPascual, Iván
dc.contributor.authorPruenza, Cristina
dc.contributor.authorBorges-Sa, Marcio
dc.contributor.authorGiglio, Andrés
dc.contributor.authorCruz-Rojo, Jaime
dc.contributor.authorPacheco-Puig, Rodrigo
dc.coverage.spatialIrlanda
dc.date.accessioned2026-08-18T16:30:40Z
dc.date.available2026-08-18T16:30:40Z
dc.date.issued2026-06-17
dc.description.abstractRecent research has increasingly focused on machine learning (ML) models for early disease prediction, yet practical frameworks for integrating these models into clinical workflows remain limited. BIAlert is a microservices-based framework designed to operate as a real-time early-warning system for ML-driven disease prediction in hospitalised patients. It can be deployed remotely on physical or virtual servers and is composed of coupled microservices that communicate through Apache Kafka queues, using HL7 FHIR resources as the message format. The system comprises four core components: (1) the Connector, which ingests raw hospital data and converts it into standardised healthcare formats; (2) the Writer, which stores FHIR-formatted data in an internal database and triggers the prediction pipeline; (3) the Predictor, which hosts ML models and generates patient-specific alerts; and (4) the Model Evaluator, which supports prospective monitoring of model performance. Alerts are displayed through the BIAlert user interface and can also be integrated directly into the electronic health record (EHR). BIAlert is currently deployed and operating in real-time clinical settings in two hospitals, demonstrating its feasibility as a scalable and interoperable solution for ML-based clinical decision support.
dc.identifier.citationInternational Journal of Medical Informatics, Vol. 219 (2026) pp. 1-9
dc.identifier.doihttps://doi.org/10.1016/j.ijmedinf.2026.106545
dc.identifier.issne1872-8243
dc.identifier.orcidhttps://orcid.org/0000-0002-0533-4531
dc.identifier.urihttps://hdl.handle.net/20.500.12254/7712
dc.language.isoes
dc.publisherElsevier
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subjectCDSS
dc.subjectMachine learning
dc.subjectSepsis
dc.titleFrom development to clinical practice: deployment of an interoperable and secure ML-based CDSS to aid in the early detection of sepsis
dc.typeArticle
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