  In the era of clinical information explosion, a good strategy for clinical
text summarization is helpful to improve the clinical workflow. The ideal
summarization strategy can preserve important information in the informative
but less organized, ill-structured clinical narrative texts. Instead of using
pure statistical learning approaches, which are difficult to interpret and
explain, we utilized knowledge of computational linguistics with human
experts-curated biomedical knowledge base to achieve the interpretable and
meaningful clinical text summarization. Our research objective is to use the
biomedical ontology with semantic information, and take the advantage from the
language hierarchical structure, the constituency tree, in order to identify
the correct clinical concepts and the corresponding negation information, which
is critical for summarizing clinical concepts from narrative text. We achieved
the clinically acceptable performance for both negation detection and concept
identification, and the clinical concepts with common negated patterns can be
identified and negated by the proposed method.
