Two-step interpretable modeling of ICU-AIs

Artif Intell Med. 2024 May;151:102862. doi: 10.1016/j.artmed.2024.102862. Epub 2024 Mar 28.


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.

PMID:38579437 | DOI:10.1016/j.artmed.2024.102862

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