Going beyond explainability in multi-modal stroke outcome prediction models
Journal:
arXiv
Published Date:
Apr 7, 2025
Abstract
Aim: This study aims to enhance interpretability and explainability of
multi-modal prediction models integrating imaging and tabular patient data.
Methods: We adapt the xAI methods Grad-CAM and Occlusion to multi-modal,
partly interpretable deep transformation models (dTMs). DTMs combine
statistical and deep learning approaches to simultaneously achieve
state-of-the-art prediction performance and interpretable parameter estimates,
such as odds ratios for tabular features. Based on brain imaging and tabular
data from 407 stroke patients, we trained dTMs to predict functional outcome
three months after stroke. We evaluated the models using different
discriminatory metrics. The adapted xAI methods were used to generated
explanation maps for identification of relevant image features and error
analysis.
Results: The dTMs achieve state-of-the-art prediction performance, with area
under the curve (AUC) values close to 0.8. The most important tabular
predictors of functional outcome are functional independence before stroke and
NIHSS on admission, a neurological score indicating stroke severity.
Explanation maps calculated from brain imaging dTMs for functional outcome
highlighted critical brain regions such as the frontal lobe, which is known to
be linked to age which in turn increases the risk for unfavorable outcomes.
Similarity plots of the explanation maps revealed distinct patterns which give
insight into stroke pathophysiology, support developing novel predictors of
stroke outcome and enable to identify false predictions.
Conclusion: By adapting methods for explanation maps to dTMs, we enhanced the
explainability of multi-modal and partly interpretable prediction models. The
resulting explanation maps facilitate error analysis and support hypothesis
generation regarding the significance of specific image regions in outcome
prediction.
