Two-step interpretable modeling of Intensive Care Acquired Infections
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913255410106368 |
|---|---|
| author | Lancia, Giacomo Varkila, Meri Cremer, Olaf Spitoni, Cristian |
| author_facet | Lancia, Giacomo Varkila, Meri Cremer, Olaf Spitoni, Cristian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_11146 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Two-step interpretable modeling of Intensive Care Acquired Infections Lancia, Giacomo Varkila, Meri Cremer, Olaf Spitoni, Cristian Applications Neural and Evolutionary Computing Machine Learning 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. |
| title | Two-step interpretable modeling of Intensive Care Acquired Infections |
| topic | Applications Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2301.11146 |