A Study into patient similarity through representation learning from medical records

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. Patient similarity analytics, Patient representation learning, Natural language processing, Health informatics 1 Introduction The patient similarity assessment identifies patients similar to a given patient. It allows physicians to gain insights into the records of matching patients and provide better treatments. Calculating patient similarity requires measuring the distance between patients within a population (1). A distance could be calculated based on various structured and unstructured data types in an electronic medical record (EMR). EMRs can be processed in the same way as general documents modeled as sequences of words. The difference is that EMRs are sequences of patient events, such as diagnoses, procedures, and medications. The representation of an EMR is a low-dimension and fixed-length embedding vector, so it can be used as an indicator to measure similarity between patients, simply like a representation of a document that can be applied to measure similarity between notes. Among previous works on patient representations based on EMRs, some have relied on structured data types (2-6), while others have only used unstructured data (7,8).