  Clinical notes containing valuable patient information are written by
different health care providers with various scientific levels and writing
styles. It might be helpful for clinicians and researchers to understand what
information is essential when dealing with extensive electronic medical
records. Entities recognizing and mapping them to standard terminologies is
crucial in reducing ambiguity in processing clinical notes. Although named
entity recognition and entity linking are critical steps in clinical natural
language processing, they can also result in the production of repetitive and
low-value concepts. In other hand, all parts of a clinical text do not share
the same importance or content in predicting the patient's condition. As a
result, it is necessary to identify the section in which each content is
recorded and also to identify key concepts to extract meaning from clinical
texts. In this study, these challenges have been addressed by using clinical
natural language processing techniques. In addition, in order to identify key
concepts, a set of popular unsupervised key phrase extraction methods has been
verified and evaluated. Considering that most of the clinical concepts are in
the form of multi-word expressions and their accurate identification requires
the user to specify n-gram range, we have proposed a shortcut method to
preserve the structure of the expression based on TF-IDF. In order to evaluate
the pre-processing method and select the concepts, we have designed two types
of downstream tasks (multiple and binary classification) using the capabilities
of transformer-based models. The obtained results show the superiority of
proposed method in combination with SciBERT model, also offer an insight into
the efficacy of general extracting essential phrase methods for clinical notes.
