{"ok":true,"nota":{"id":"ba87d971890c81a9","slug":"una-ia-que-busca-causas-y-no-solo-coincidencias-mejora-en-predecir-rei-ba87d9","titulo":"Una IA que busca causas y no solo coincidencias mejora en predecir reingresos a terapia intensiva","resumen":"Compararon un modelo de IA que solo detecta correlaciones con otro que intenta incorporar relaciones de causa y efecto, ambos para predecir reingresos a terapia intensiva. El objetivo era ver cuál explica mejor sus resultados y generaliza a otros pacientes.","fuente":"zenodo","fuenteNombre":"Zenodo · Investigación abierta","color":"#1682d4","autor":"Remoundou, Konstantina, KOUMANTAKIS, EMANUELE, Ioanna Roussaki","url":"https://doi.org/10.3390/healthcare14142067","imagen":"/hoy/portada/ba87d971890c81a9","tema":"ciencia","subtemas":["inteligencia artificial","causalidad","terapia intensiva","reingresos hospitalarios","sesgos en algoritmos"],"entidades":["Zenodo","terapia intensiva"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-20T07:20:24.000Z","indexTs":"2026-10-22T10:32:02.032Z","hash":"531baf495769fd30a1cae749b6ba7079777c307c24cf70dc74bd7508b40105f9","tsa":{"time":"Oct 22 10:32:41 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"Una IA que solo encuentra coincidencias puede equivocarse cuando cambian las condiciones del hospital. Un modelo que intenta entender causas tiene más chances de funcionar en otro lugar. Eso importa en Argentina, donde un sistema entrenado en un hospital porteño no necesariamente sirve en una provincia. Un algoritmo que no sabe por qué acierta puede fallar justo cuando más se lo necesita.","quienFirma":"Zenodo (repositorio abierto); autores no identificados en el registro","confianza":"preprint","consulta":null},"sello":{"hash":"531baf495769fd30a1cae749b6ba7079777c307c24cf70dc74bd7508b40105f9","sello":"e9daaacc05205a8eef122b83d312bc9797f7ebb08614c9135ed9492237637fc1","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6574471,"aleatorio":"8dc65bda024d9b15bc5015dca7a625bd58abcd79898d9e649dd88c7b09d4609e","tiempo":"2026-10-22T10:32:30.000Z"},"nist":{"cadena":2,"pulso":1994441,"salida":"917B00588BBAF41DD8C461E7D93EA47DC73C00983776285722380061C2505D68D1C49D178A1E9E1A422EEA35DB6367141AB79828DD0F9592CE1805F752B02469","tiempo":"2026-10-22T10:32:00.000Z"},"noAntes":"2026-10-22T10:32:30.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 22 10:32:41 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"Mitigating Clinical Confounding in AI Models: A Comparative Analysis of Associative and Causal-Fused AI for ICU Readmissions","descripcion":"<p>In critical environments, patient discharge decisions present significant chal-lenges, particularly regarding the prevention of readmissions. However, traditional AIsystems focus on correlations rather than causation, resulting in issues related to explain-ability and generalization. This study conducts a comparative evaluation between standardpredictive and causal-fused models to assess their ability to mitigate clinical confoundingon predicting readmissions.</p>","pubDate":"2026-10-20T07:20:24Z","url":"https://doi.org/10.3390/healthcare14142067"},"indexado":"2026-10-22T10:32:02.032Z"}}