  Healthcare is becoming a more and more important research topic recently.
With the growing data in the healthcare domain, it offers a great opportunity
for deep learning to improve the quality of medical service. However, the
complexity of electronic health records (EHR) data is a challenge for the
application of deep learning. Specifically, the data produced in the hospital
admissions are monitored by the EHR system, which includes structured data like
daily body temperature, and unstructured data like free text and laboratory
measurements. Although there are some preprocessing frameworks proposed for
specific EHR data, the clinical notes that contain significant clinical value
are beyond the realm of their consideration. Besides, whether these different
data from various views are all beneficial to the medical tasks and how to best
utilize these data remain unclear. Therefore, in this paper, we first extract
the accompanying clinical notes from EHR and propose a method to integrate
these data, we also comprehensively study the different models and the data
leverage methods for better medical task prediction. The results on two medical
prediction tasks show that our fused model with different data outperforms the
state-of-the-art method that without clinical notes, which illustrates the
importance of our fusion method and the value of clinical note features. Our
code is available at https: //github.com/emnlp-mimic/mimic.
