  Recent rapid increase in the generation of clinical data and rapid
development of computational science make us able to extract new insights from
massive datasets in healthcare industry. Oncological clinical notes are
creating rich databases for documenting patients history and they potentially
contain lots of patterns that could help in better management of the disease.
However, these patterns are locked within free text (unstructured) portions of
clinical documents and consequence in limiting health professionals to extract
useful information from them and to finally perform Query and Answering (QA)
process in an accurate way. The Information Extraction (IE) process requires
Natural Language Processing (NLP) techniques to assign semantics to these
patterns. Therefore, in this paper, we analyze the design of annotators for
specific lung cancer concepts that can be integrated over Apache Unstructured
Information Management Architecture (UIMA) framework. In addition, we explain
the details of generation and storage of annotation outcomes.
