Assessing the Clinical Validity of Attention-based and SHAP Temporal Explanations for Adverse Drug Event Predictions

Attention mechanisms form the basis of providing temporal explanations for a variety of state-of-the-art recurrent neural network (RNN) based architectures. However, evidence is lacking that attention mechanisms are capable of providing sufficiently valid medical explanations. In this study we focus on the quality of temporal explanations for the medical problem of adverse drug event (ADE) prediction by comparing explanations globally and locally provided by an attention-based RNN architecture against those provided by more a more basic RNN using the post-hoc SHAP framework, a popular alternative option which adheres to several desirable explainability properties. The validity of this comparison is supported by medical expert knowledge gathered for the purpose of this study. This investigation has uncovered that these explanation methods both possess appropriateness for ADE explanations and may be used complementarily, due to SHAP providing more clinically appropriate global explanations and attention mechanisms capturing more clinically appropriate local explanations. Additional feedback from medical experts reveal that SHAP may be more applicable to real-time clinical encounters, in which efficiency must be prioritised, over attention explanations which possess properties more appropriate for offline analyses.