Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

• Published in: PloS one Vol. 15; no. 4; p. e0231166
• Main Authors: Zihni, Esra, Madai, Vince Istvan, Livne, Michelle, Galinovic, Ivana, Khalil, Ahmed A., Fiebach, Jochen B., Frey, Dietmar
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.04.2020; Public Library of Science (PLoS)
• Subjects: Age; Aged; Aged, 80 and over; Algorithms; Area Under Curve; Artificial intelligence; Artificial neural networks; Biology and Life Sciences; Clinical Decision-Making - methods; Computer and Information Sciences; Datasets; Decision support systems; Diabetes; Domains; Engineering and Technology; Ethics; Female; Forecasting; Generalized linear models; Humans; Learning algorithms; Logistic Models; Machine learning; Male; Medicine; Medicine and Health Sciences; Methods; Middle Aged; Multilayer perceptrons; Neural networks; Neural Networks, Computer; Outcome Assessment, Health Care; Patients; Physical Sciences; Physicians; Prediction models; Predictions; Prognosis; Regression analysis; Regression models; Regularization; Research and Analysis Methods; Researchers; Retrospective Studies; State-of-the-art reviews; Stroke; Stroke - diagnosis; Supervised Machine Learning - trends; Values; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0231166

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 tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods.