DOI: 10.1101/19010975Nov 4, 2019Paper

Opening the Black Box of Artificial Intelligence for Clinical Decision Support: A Study Predicting Stroke Outcome

MedRxiv : the Preprint Server for Health Sciences
E. ZihniD. Frey

Abstract

State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features importance with regard to predictions using deep taylor decomposition for MLP, shapley values for tr...Continue Reading

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