SIDTER: Prototype Early diagnosis system for respiratory diseases assisted by AI with human supervision in the process

Página de ítem simple
cic.institucionOrigenLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.isFulltextSI
cic.isPeerReviewedSI
cic.lugarDesarrolloLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.parentTypeObjeto de conferencia
cic.versionAceptada
dc.date.accessioned2026-03-25T12:10:48Z
dc.date.available2026-03-25T12:10:48Z
dc.identifier.urihttps://digital.cic.gba.gob.ar/handle/11746/12675
dc.titleSIDTER: Prototype Early diagnosis system for respiratory diseases assisted by AI with human supervision in the processen
dc.typeDocumento de conferencia
dcterms.abstractHuman health is a fundamental pillar of individual and collective well-being, as it determines people’s ability to reach their potential, contribute to social progress and carry out daily activities. According to the World Health Organization (WHO), health implies not only the absence of disease, but also a complete state of physical, mental and social well-being. Respiratory diseases such as influenza, the common cold, COPD, asthma, pneumonia and allergic rhinitis significantly impact human health by compromising respiratory function, generating acute symptoms and causing chronic complications. These conditions reduce physical capacity, impair quality of life and generate socioeconomic burdens. Early and accurate diagnosis is essential to mitigate their impact, as diseases such as COPD affect more than 200 million people worldwide. However, challenges such as limited access to medical care, unspecified symptoms, continuous exposure to risk factors and delays in referral to specialized centers still persist. In this context, artificial intelligence (AI) presents itself as a key ally for early diagnosis, improving clinical accuracy and optimizing time-consuming tasks. In response to these challenges, SIDTER is presented: a prototype AI-assisted early diagnosis system for respiratory diseases with medical supervision and validation. It aims to support physicians, improve clinical diagnostic capabilities and strengthen patient-physician interaction.en
dcterms.creator.authorManquillo, Juan Sebastián
dcterms.creator.authorMuñoz Carvajal, Sebastián
dcterms.creator.authorGiraldo Muñoz, Juan Diego
dcterms.creator.authorRestrepo, Yeison Daniel
dcterms.creator.authorBeru, Robert Alejandro
dcterms.creator.authorMogollon, Yilmar
dcterms.creator.authorLópez Erazo, Oscar Santiago
dcterms.creator.authorLópez Erazo, Juliana Maria
dcterms.creator.authorDelle Ville, Juliana
dcterms.creator.authorMuñoz, Luis Freddy
dcterms.creator.authorAntonelli, Leandro
dcterms.creator.authorCollazos, César
dcterms.isPartOf.series20 Congreso Colombiano de Computacion (Colombia, 12 al 14 de agosto de 2026)
dcterms.issued2026
dcterms.languageInglés
dcterms.licenseAttribution-NonCommercial-NoDerivatives 4.0 International (BY-NC-ND 4.0)
dcterms.subjectRespiratory Diseasesen
dcterms.subjectAIen
dcterms.subjectRandom Foresten
dcterms.subjectDiagnosisen
dcterms.subjectHuman-Patient Interactionen
dcterms.subject.materiaCiencias de la Computación e Información

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
SIDTER Prototype Early diagnosis.pdf-PDFA.pdf
Tamaño:
614.98 KB
Formato:
Adobe Portable Document Format
Descripción:
Documento completo
Descargar

Bloque de licencias

Mostrando 1 - 1 de 1
Miniatura
Nombre:
license.txt
Tamaño:
3.46 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

Artículos y presentaciones en Congresos LIFIA