  Disease risk prediction has attracted increasing attention in the field of
modern healthcare, especially with the latest advances in artificial
intelligence (AI). Electronic health records (EHRs), which contain
heterogeneous patient information, are widely used in disease risk prediction
tasks. One challenge of applying AI models for risk prediction lies in
generating interpretable evidence to support the prediction results while
retaining the prediction ability. In order to address this problem, we propose
the method of jointly embedding words and labels whereby attention modules
learn the weights of words from medical notes according to their relevance to
the names of risk prediction labels. This approach boosts interpretability by
employing an attention mechanism and including the names of prediction tasks in
the model. However, its application is only limited to the handling of textual
inputs such as medical notes. In this paper, we propose a label dependent
attention model LDAM to 1) improve the interpretability by exploiting
Clinical-BERT (a biomedical language model pre-trained on a large clinical
corpus) to encode biomedically meaningful features and labels jointly; 2)
extend the idea of joint embedding to the processing of time-series data, and
develop a multi-modal learning framework for integrating heterogeneous
information from medical notes and time-series health status indicators. To
demonstrate our method, we apply LDAM to the MIMIC-III dataset to predict
different disease risks. We evaluate our method both quantitatively and
qualitatively. Specifically, the predictive power of LDAM will be shown, and
case studies will be carried out to illustrate its interpretability.
