Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A
  Case Study on Hemorrhagic Stroke
Qizhang Feng Jiayi Yuan Forhan Bin Emdad Karim Hanna Xia Hu Zhe He
Stroke is a significant cause of mortality and morbidity, necessitating early
predictive strategies to minimize risks. Traditional methods for evaluating
patients, such as Acute Physiology and Chronic Health Evaluation (APACHE II,
IV) and Simplified Acute Physiology Score III (SAPS III), have limited accuracy
and interpretability. This paper proposes a novel approach: an interpretable,
attention-based transformer model for early stroke mortality prediction. This
model seeks to address the limitations of previous predictive models, providing
both interpretability (providing clear, understandable explanations of the
model) and fidelity (giving a truthful explanation of the model's dynamics from
input to output). Furthermore, the study explores and compares fidelity and
interpretability scores using Shapley values and attention-based scores to
improve model explainability. The research objectives include designing an
interpretable attention-based transformer model, evaluating its performance
compared to existing models, and providing feature importance derived from the
model.