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Data Fusion of Medical Records and Clinical Data to Enhance Tuberculosis Diagnosis in Resource-Limited Settings

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dc.creatorPalenci, María A.spa
dc.creatorVergara, Erikaspa
dc.creatorAwad, Carlo E.spa
dc.creatorJutinico, Andrés L.spa
dc.creatorRomero-Gomez, Andrés F.spa
dc.creatorOrjuela Cañón, Alvaro Davidspa
dc.date.accessioned2025-07-21T16:35:01Z
dc.date.available2025-07-21T16:35:01Z
dc.date.created2025-05-01spa
dc.date.issued2025-05-01spa
dc.description.abstractTuberculosis (TB) is an infectious disease that has been declared a global emergency by the World Health Organization and remains one of the top ten causes of death worldwide. TB diagnosis is particularly challenging in developing countries, where limited infrastructure for detection and treatment complicates efforts to control the disease. These resource constraints are especially critical in remote areas with few mechanisms for timely diagnosis, which is essential for effective patient management. Artificial intelligence (AI) has emerged as a valuable tool in supporting health professionals by enhancing diagnostic processes. This paper explores the use of natural language processing (NLP) techniques and machine learning (ML) models to facilitate TB diagnosis in settings where robust data infrastructure is unavailable. Two distinct data sources were analyzed: text extracted from electronic medical records (EMRs) and patient clinical data (CD). Four different ML-based approaches were implemented: two models using each data source independently and two data fusion models combining both sources. The relevance of these strategies was assessed in collaboration with physicians to ensure their practical applicability in clinical decision-making. The results of the data fusion models were compared to determine which source provided more valuable diagnostic information. The best-performing model, which relied solely on CD, achieved a sensitivity of 73% , outperforming smear microscopy, which typically ranges from 40% to 60% . These findings underscore the importance of analyzing physicians’ reports and assessing the availability of such information alongside structured clinical data. This approach is particularly beneficial in resource-limited settings, where access to comprehensive clinical data may be restricted.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.3390/app15105423spa
dc.identifier.issn2662-4435spa
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/46049
dc.language.isoengspa
dc.publisherMDPIspa
dc.relation.ispartofApplied Sciences. 2025, 15(10), 5423spa
dc.relation.urihttps://www.mdpi.com/2076-3417/15/10/5423spa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accesoAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.sourceApplied Sciencesspa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordQuímica de procesos y tecnologíaeng
dc.titleData Fusion of Medical Records and Clinical Data to Enhance Tuberculosis Diagnosis in Resource-Limited Settingsspa
dc.typearticlespa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.type.spaArtículo de Investigaciónspa
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