Two-step interpretable modeling of ICU-AIs.
Artif Intell Med
; 151: 102862, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38579437
ABSTRACT
We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold to improve the predictive power while maintaining the interpretability of the models. To go beyond the black box paradigm of artificial neural networks, we propose a parsimonious and robust semi-parametric approach (i.e., a landmarking competing risks model) that combines routinely collected low-resolution data with predictive features extracted from a convolutional neural network, that was trained on high resolution time-dependent information. We then use saliency maps to analyze and explain the extra predictive power of this model. To illustrate our methodology, we focus on healthcare-associated infections in patients admitted to an intensive care unit.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Unidades de Terapia Intensiva
Limite:
Humans
Idioma:
En
Revista:
Artif Intell Med
Ano de publicação:
2024
Tipo de documento:
Article