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Modos de Fallo de los Algoritmos de Interpretabilidad de Series Temporales para Aplicaciones de Cuidados Críticos y
Shashank Yadav1, Vignesh Subbian1
1College of Engineering, University of Arizona, Tucson, AZ.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
Resumen
Los métodos de interpretabilidad de aprendizaje profundo tienen dificultades con los datos dinámicos del paciente en cuidados intensivos. Los enfoques basados en máscaras aprendibles ofrecen una importancia de características más confiable para las predicciones de series temporales.
Área de la Ciencia:
- Inteligencia Artificial; Informática Clínica; Ingeniería Biomédica
Sus antecedentes:
- Los modelos de aprendizaje profundo son cruciales para las predicciones de supervivencia de pacientes en cuidados intensivos.
- Los métodos de interpretabilidad existentes enfrentan desafíos con datos de pacientes dinámicos y variables en el tiempo.
- La dependencia del objetivo variable en el tiempo y la suavidad temporal son problemas clave para los algoritmos actuales.
Objetivo del estudio:
- Analizar los modos de fallo de los algoritmos de interpretabilidad comunes en tareas de predicción dinámica.
- Proponer marcos basados en máscaras aprendibles como alternativas superiores para aplicaciones de cuidados críticos.
- Mejorar la confiabilidad y consistencia de la interpretación de la importancia de las características a lo largo del tiempo.
Principales métodos:
- Análisis sistemático de los métodos de interpretabilidad basados en Gradiente, Oclusión y Permutación.
- Evaluación de los modos de fallo en escenarios de predicción dinámica de series temporales.
- Desarrollo y propuesta de marcos de interpretabilidad basados en máscaras aprendibles.
Principales resultados:
- Los métodos comunes de interpretabilidad exhiben limitaciones significativas con datos variables en el tiempo.
- Los marcos basados en máscaras aprendibles pueden incorporar continuidad temporal y consistencia de etiquetas.
- Estos marcos alternativos proporcionan una importancia de características más consistente a lo largo del tiempo.
Conclusiones:
- La interpretabilidad basada en máscaras aprendibles es más confiable para la predicción dinámica de series temporales en cuidados intensivos.
- Este enfoque aborda las limitaciones de los métodos tradicionales en las condiciones cambiantes del paciente.
- La interpretabilidad mejorada apoya una mejor alineación y despliegue de la IA en entornos de cuidados intensivos.
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