Two-step interpretable modeling of Intensive Care Acquired Infections

• URL: http://arxiv.org/abs/2301.11146v2
• Date: Wed, 6 Mar 2024 13:46:06 GMT
• Title: Two-step interpretable modeling of Intensive Care Acquired Infections
• Authors: Giacomo Lancia, Meri Varkila, Olaf Cremer, Cristian Spitoni
• Abstract summary: 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 interpretability of the models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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