  Due to the widespread use of tools and the development of text processing
techniques, the size and range of clinical data are not limited to structured
data. The rapid growth of recorded information has led to big data platforms in
healthcare that could be used to improve patients' primary care and serve
various secondary purposes. Patient similarity assessment is one of the
secondary tasks in identifying patients who are similar to a given patient, and
it helps derive insights from similar patients' records to provide better
treatment. This type of assessment is based on calculating the distance between
patients. Since representing and calculating the similarity of patients plays
an essential role in many secondary uses of electronic records, this article
examines a new data representation method for Electronic Medical Records (EMRs)
while taking into account the information in clinical narratives for similarity
computing. Some previous works are based on structured data types, while other
works only use unstructured data. However, a comprehensive representation of
the information contained in the EMR requires the effective aggregation of both
structured and unstructured data. To address the limitations of previous
methods, we propose a method that captures the co-occurrence of different
medical events, including signs, symptoms, and diseases extracted via
unstructured data and structured data. It integrates data as discriminative
features to construct a temporal tree, considering the difference between
events that have short-term and long-term impacts. Our results show that
considering signs, symptoms, and diseases in every time interval leads to less
MSE and more precision compared to baseline representations that do not
consider this information or consider them separately from structured data.
