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Un nuevo modelo fundacional, SleepFM, analiza datos de sueño para predecir más de 130 enfermedades, incluyendo mortalidad y demencia, a partir de una sola noche de sueño. Esto avanza la comprensión del papel del sueño en la salud y la predicción de enfermedades.

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Área de la Ciencia:

  • Informática Biomédica
  • Medicina del Sueño
  • Inteligencia Artificial

Sus antecedentes:

  • El sueño es crucial para la salud, pero su vínculo con las enfermedades es complejo y los datos de polisomnografía (PSG) están infrautilizados.
  • Los métodos actuales de análisis de PSG enfrentan desafíos en la estandarización, generalización e integración de datos multimodales.

Objetivo del estudio:

  • Desarrollar un modelo fundacional multimodal de sueño (SleepFM) para superar las limitaciones del análisis de PSG.
  • Permitir la predicción precisa del riesgo futuro de enfermedades utilizando datos de sueño.

Principales métodos:

  • Desarrolló SleepFM utilizando un novedoso enfoque de aprendizaje contrastivo para datos multimodales de PSG.
  • Entrenado con más de 585 000 horas de grabaciones de PSG de ~65 000 participantes.
  • Utilizó representaciones latentes del sueño para la predicción de enfermedades y el aprendizaje por transferencia.

Principales resultados:

  • SleepFM predice con precisión 130 afecciones (Índice C ≥ 0,75), incluyendo mortalidad, demencia, infarto de miocardio y enfermedad renal crónica.
  • Demostró un sólido aprendizaje por transferencia en un conjunto de datos independiente.
  • Logró un rendimiento competitivo con modelos especializados para la estadificación del sueño y la clasificación de la apnea del sueño.

Conclusiones:

  • Los modelos fundacionales pueden aprender eficazmente de grabaciones de sueño multimodales.
  • SleepFM permite el análisis del sueño y la predicción de enfermedades a escala y con poca necesidad de etiquetas.
  • Este enfoque mejora nuestra comprensión del impacto del sueño en la salud física y mental.