  Patient similarity assessment, which identifies patients similar to a given
patient, can help improve medical care. The assessment can be performed using
Electronic Medical Records (EMRs). Patient similarity measurement requires
converting heterogeneous EMRs into comparable formats to calculate their
distance. While versatile document representation learning methods have been
developed in recent years, it is still unclear how complex EMR data should be
processed to create the most useful patient representations. This study
presents a new data representation method for EMRs that takes the information
in clinical narratives into account. To address the limitations of previous
approaches in handling complex parts of EMR data, an unsupervised method is
proposed for building a patient representation, which integrates unstructured
data with structured data extracted from patients' EMRs. In order to model the
extracted data, we employed a tree structure that captures the temporal
relations of multiple medical events from EMR. We processed clinical notes to
extract symptoms, signs, and diseases using different tools such as medspaCy,
MetaMap, and scispaCy and mapped entities to the Unified Medical Language
System (UMLS). After creating a tree data structure, we utilized two novel
relabeling methods for the non-leaf nodes of the tree to capture two temporal
aspects of the extracted events. By traversing the tree, we generated a
sequence that could create an embedding vector for each patient. The
comprehensive evaluation of the proposed method for patient similarity and
mortality prediction tasks demonstrated that our proposed model leads to lower
mean squared error (MSE), higher precision, and normalized discounted
cumulative gain (NDCG) relative to baselines.
