  Early prediction of mortality and length of stay(LOS) of a patient is vital
for saving a patient's life and management of hospital resources. Availability
of electronic health records(EHR) makes a huge impact on the healthcare domain
and there has seen several works on predicting clinical problems. However, many
studies did not benefit from the clinical notes because of the sparse, and high
dimensional nature. In this work, we extract medical entities from clinical
notes and use them as additional features besides time-series features to
improve our predictions. We propose a convolution based multimodal
architecture, which not only learns effectively combining medical entities and
time-series ICU signals of patients, but also allows us to compare the effect
of different embedding techniques such as Word2vec, FastText on medical
entities. In the experiments, our proposed method robustly outperforms all
other baseline models including different multimodal architectures for all
clinical tasks. The code for the proposed method is available at
https://github.com/tanlab/ConvolutionMedicalNer.
