关键词: 层次注意力机制, 循环神经网络, 疾病预警, 可解释性

Abstract:

Accuracy and interpretability are two factors that determine whether the prediction model can be applied successfully. Traditional statistical analysis models, such as Logistic regression, are widely used because it comprehends easily, although the prediction isn’t accurate. In contrast, the deep learning “black box model” based on RNN or CNN has high accuracy but is often difficult to understand. The balancing of these factors in the medical field is a great challenge for current research. This paper intends to establish an Interpretable Hierarchical Attention Network（IHAN） based on output optimization to give early warning of the possible severe and critical disease of patients in rescue process through the experimental analysis of CIS system data collected in a hospital. IHAN is superior to other neural network models in terms of experimental accuracy, and can imitate human behavior. It focuses on abnormalities in the two dimensions of time and risk factors in patients’ physiological data, and achieves a better interpretability while maintaining a higher accuracy.

Key words: hierarchical attention mechanism, Recurrent Neural Network（RNN）, early warning of disease, interpretability