  A key component of deep learning (DL) for natural language processing (NLP)
is word embeddings. Word embeddings that effectively capture the meaning and
context of the word that they represent can significantly improve the
performance of downstream DL models for various NLP tasks. Many existing word
embeddings techniques capture the context of words based on word co-occurrence
in documents and text; however, they often cannot capture broader
domain-specific relationships between concepts that may be crucial for the NLP
task at hand. In this paper, we propose a method to integrate external
knowledge from medical terminology ontologies into the context captured by word
embeddings. Specifically, we use a medical knowledge graph, such as the unified
medical language system (UMLS), to find connections between clinical terms in
cancer pathology reports. This approach aims to minimize the distance between
connected clinical concepts. We evaluate the proposed approach using a
Multitask Convolutional Neural Network (MT-CNN) to extract six cancer
characteristics -- site, subsite, laterality, behavior, histology, and grade --
from a dataset of ~900K cancer pathology reports. The results show that the
MT-CNN model which uses our domain informed embeddings outperforms the same
MT-CNN using standard word2vec embeddings across all tasks, with an improvement
in the overall micro- and macro-F1 scores by 4.97\%and 22.5\%, respectively.
