{"ok":true,"nota":{"id":"5e26e285bfd3fb7c","slug":"una-inteligencia-artificial-anticipa-que-efectos-adversos-puede-tener--5e26e2","titulo":"Una inteligencia artificial anticipa qué efectos adversos puede tener un paciente con cáncer","resumen":"Desarrollaron SPARC, un modelo de inteligencia artificial entrenado con más de 100 millones de mediciones de salud de 224.047 pacientes con cáncer. Logró predecir mejor que otros métodos qué pacientes tienen más riesgo de efectos adversos del tratamiento, en varios grupos de prueba.","fuente":"medrxiv","fuenteNombre":"medRxiv · preprints","color":"#1f7a4d","autor":"Fu, C., Yu, J., Shi, R., Waters, M., Pavlak, M., Arfe, A., Shah, S. P., Battle, A., kehl, K. L., Bot, B. M., US Cancer A","url":"https://www.medrxiv.org/content/10.64898/2026.10.22.26364879v1?rss=1","imagen":"/hoy/portada/5e26e285bfd3fb7c","tema":"salud","subtemas":["inteligencia artificial","oncologia","efectos adversos","historia clinica","prediccion de riesgo"],"entidades":["SPARC","medRxiv"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-26T00:00:00.000Z","indexTs":"2026-10-26T16:35:59.047Z","hash":"fc80c59e82a6474aab5cb366f7a50b8b4704d613ab73ca22ad62e07eb04bbda2","tsa":{"time":"Oct 26 16:36:53 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"El modelo aprendió de datos reales de historias clínicas, no de casos armados en laboratorio, y acertó en pacientes nuevos de otro centro y de bases públicas externas. Si los médicos identifican a tiempo quién va a tener una reacción grave, pueden intervenir antes y evitar internaciones, costos y sufrimiento. Para un paciente oncológico argentino, esto puede significar que alguien se anticipe a un problema que hoy se detecta tarde. Igual, la herramienta todavía tiene que probarse en los hospitales de acá antes de llegar a la consulta.","quienFirma":"Preprint en medRxiv (institución principal no identificada en el resumen)","confianza":"preprint","consulta":null},"sello":{"hash":"fc80c59e82a6474aab5cb366f7a50b8b4704d613ab73ca22ad62e07eb04bbda2","sello":"d135920b09ee8e5b6bf0df72524e5fddda0011ec09d663ffb34c6dfee0b362f9","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6586719,"aleatorio":"69c104ecd3cb13434712414db5b2ad3c4f3e8c23a6e958cf0c536314410db9d8","tiempo":"2026-10-26T16:36:30.000Z"},"nist":{"cadena":2,"pulso":2000553,"salida":"C52F940F6CB3ED0E0B156BE5FA3EFA2B75E456473C90354367E414D5115CEDF0D632503093B1A864228DF7BF37C390F188E14CB6127FE2E67FC4E9BF01B2FA0B","tiempo":"2026-10-26T16:36:00.000Z"},"noAntes":"2026-10-26T16:36:30.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 26 16:36:53 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"Longitudinal AI Models Predict Cancer Adverse Events","descripcion":"Clinical interventions for cancer treatment toxicities can reduce morbidity, mortality, and healthcare cost, if high risk patients can be identified. We developed SPARC, a transformer-based foundation model, using real-world, longitudinal, routinely collected health measurements, to predict adverse event risk. Pre-training and fine-tuning SPARC on 101,920,379 measurements from 224,047 patients with cancer led to state-of-the-art performance at predicting cancer-related adverse events on (i) held-out data from the development cohort, (ii) a prospective validation cohort from the same center (Npatients=23,126), (iii) multiple publicly available external datasets (Npatients=26,012), and (iv) ex","pubDate":"2026-10-26","url":"https://www.medrxiv.org/content/10.64898/2026.10.22.26364879v1?rss=1"},"indexado":"2026-10-26T16:35:59.047Z"}}