  Today, we are seeing an ever-increasing number of clinical notes that contain
clinical results, images, and textual descriptions of patient's health state.
All these data can be analyzed and employed to cater novel services that can
help people and domain experts with their common healthcare tasks. However,
many technologies such as Deep Learning and tools like Word Embeddings have
started to be investigated only recently, and many challenges remain open when
it comes to healthcare domain applications. To address these challenges, we
propose the use of Deep Learning and Word Embeddings for identifying sixteen
morbidity types within textual descriptions of clinical records. For this
purpose, we have used a Deep Learning model based on Bidirectional Long-Short
Term Memory (LSTM) layers which can exploit state-of-the-art vector
representations of data such as Word Embeddings. We have employed pre-trained
Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained
on the target domain. Furthermore, we have compared the performances of the
deep learning approaches against the traditional tf-idf using Support Vector
Machine and Multilayer perceptron (our baselines). From the obtained results it
seems that the latter outperforms the combination of Deep Learning approaches
using any word embeddings. Our preliminary results indicate that there are
specific features that make the dataset biased in favour of traditional machine
learning approaches.
