Actualización sobre modelos predictivos de manifestaciones graves y mortalidad por COVID-19
Palabras clave:
COVID-19, Gravedad, Modelos predictivos, Resultados de la enfermedadResumen
En este artículo presentamos una revisión de los modelos basados en Inteligencia Artificial (IA), orientados a la predicción de manifestaciones graves provocadas por el virus SARS-COV2. El objetivo de esta revisión fue evaluar los hallazgos más relevantes publicados entre 2020 y 2023, que puedan servir de base al desarrollo de un modelo propio ajustado a las condiciones de nuestro país, caracterizadas por la ausencia de un modelo propio ajustado a la ausencia de una base de datos de datos clínicos bien estructurada y con la presencia de estudios radiológicos apoyados por imágenes de rayos X de tórax (CXR) y tomografía computarizada (CT). Realizamos una revisión sistemática para resumir y evaluar críticamente los estudios disponibles que han desarrollado modelos de pronóstico de COVID-19 basados en IA, que predicen resultados de salud, sobre todo en modelos que utilizan como base las imágenes CXR y CT. Se realizaron búsquedas en tres bases de datos bibliográficas, para identificar artículos publicados sobre modelos de pronóstico que predijeran resultados adversos en pacientes adultos con COVID- 19, incluido el ingreso a la unidad de cuidados intensivos, la necesidad de ventilación mecánica y la mortalidad.
El estudio demostró que los modelos basados en aprendizaje profundo, que utilizan imágenes CXR o CT y su combinación con datos clínicos no complejos, pueden alcanzar un rendimiento significativo en la predicción. Por ello, aquí proponemos una estrategia para abordar este desafío, según las condiciones de nuestro país, combinando la clasificación del grado de gravedad de la afectación pulmonar en imágenes de CXR, datos clínicos de comorbilidades y datos biográficos.
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Derechos de autor 2023 Eduardo Garea Llano, Evelio Gonzalez Dalmau
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
