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Predicción del delirium postoperatorio tras resección de cáncer de pulmón: utilidad del algoritmo LIME y datos
Yu Zhu1,2, Renrui Liang3, Jian-Jun Yang4
1Department of Anaesthesiology, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China.
Scientific reports
|January 29, 2026
Resumen
Este estudio utilizó eficazmente la inteligencia artificial y datos sintéticos para predecir el delirium postoperatorio (DP) tras la resección de cáncer de pulmón. La Gaussiana Naive Bayes mostró una alta precisión e identificó factores de riesgo clave para mejorar los resultados del paciente.
Sus antecedentes:
- El delirium postoperatorio (DP) tras la resección de cáncer de pulmón presenta un desafío clínico significativo.
- Los modelos predictivos existentes a menudo luchan con tamaños de muestra pequeños, lo que limita su utilidad clínica.
- Existe la necesidad de modelos robustos impulsados por IA para predecir el DP, especialmente después de la neumonectomía.
Conclusiones:
- Los datos sintéticos apoyan eficazmente el entrenamiento de modelos de IA para la predicción del DP, incluso con datos limitados del mundo real.
- El GNB demuestra un rendimiento sólido y es un algoritmo candidato potencial para la predicción del DP.
- El estudio destaca la viabilidad de la IA y LIME en el desarrollo de modelos predictivos de DP precisos e interpretables.
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