{"ok":true,"nota":{"id":"476353ebf10495e7","slug":"un-sistema-con-ia-cruza-datos-de-medicamentos-y-reportes-de-efectos-ad-476353","titulo":"Un sistema con IA cruza datos de medicamentos y reportes de efectos adversos para detectar riesgos","resumen":"Investigadores desarrollaron un método que combina inteligencia artificial con reportes de la FDA, literatura científica, ensayos clínicos e historias clínicas de un hospital de Boston para integrar información sobre seguridad de medicamentos. El trabajo describe el marco y sus capacidades de análisis.","fuente":"medrxiv","fuenteNombre":"medRxiv · preprints","color":"#1f7a4d","autor":"Li, Y., Zhou, H., Yeh, F.-Y., Sun, Z., Zheng, J., Alzaidi, S., Guan, H., Li, J., Seger, D. L., Wang, L., Li, M., Li, X.,","url":"https://www.medrxiv.org/content/10.64898/2026.10.20.26365969v1?rss=1","imagen":"/hoy/portada/476353ebf10495e7","tema":"salud","subtemas":["farmacovigilancia","inteligencia artificial","efectos adversos","seguridad de medicamentos","historias clínicas"],"entidades":["Mass General Brigham","FDA","FAERS"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-21T00:00:00.000Z","indexTs":"2026-10-22T04:37:56.220Z","hash":"d40070063c3af385313953207c362ab08a73737a2d5daeb0fb59a087b02a1f10","tsa":{"time":"Oct 22 04:38:37 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"Hoy la información sobre efectos adversos de fármacos está dispersa en fuentes con formatos y calidades muy distintas, y eso hace lenta la detección de problemas. Un sistema que ordene esa evidencia podría acelerar alertas sobre medicamentos que usan miles de argentinos. Igual, es un preprint que todavía no pasó revisión por pares, y la validación real con pacientes locales falta.","quienFirma":"Mass General Brigham","confianza":"preprint","consulta":null},"sello":{"hash":"d40070063c3af385313953207c362ab08a73737a2d5daeb0fb59a087b02a1f10","sello":"f67e4a571adde249cd57ada4fdc2360357ce1805a4fca23ba6232ae56fb900bc","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6573763,"aleatorio":"ac1ab9766ef209323093f9521e7402b29370fa8f04af200361451518fae9c9b2","tiempo":"2026-10-22T04:38:30.000Z"},"nist":{"cadena":2,"pulso":1994087,"salida":"2CAE0B0247F8B2C1365D5848B4971A88B86F8096596AB659C9B46851CC37B71B43230BE62392778D57CA4D0DAE3440419DC67D4E27A84A89E3AA5B314736F2A3","tiempo":"2026-10-22T04:38:00.000Z"},"noAntes":"2026-10-22T04:38:30.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 22 04:38:37 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"AI-Empowered Multimodal Data Integration and Knowledge Discovery for Pharmacovigilance","descripcion":"Biomedical artificial intelligence (AI) increasingly relies on heterogeneous data sources, yet differences in data structure, terminology, evidence quality, and provenance complicate their integration into reliable knowledge resources. Here, we develop an evidence-aware framework for processing and integrating heterogeneous pharmacovigilance data from FDA Adverse Event Reporting System (FAERS) reports, biomedical literature, clinical trials, and electronic health records in Mass General Brigham. The framework combines large language model (LLM)-based information extraction, ontology-driven semantic normalization, cross-source evidence assessment, statistical and machine learning-based priori","pubDate":"2026-10-21","url":"https://www.medrxiv.org/content/10.64898/2026.10.20.26365969v1?rss=1"},"indexado":"2026-10-22T04:37:56.220Z"}}