Patient Similarity via Joint Embeddings of Medical Knowledge Graph and Medical Entity Descriptions

With the prevalence and growing volume of Electronic Health Records (EHRs), there has been increasing interest in mining EHRs for improving clinical decision support. The accurate identification of patients with similar conditions based on EHRs is a key step in personalized healthcare. Existing studies model EHRs by medical knowledge graph embedding to learn the latent embeddings of medical entities (e.g., patients, medications, diagnoses and procedures). However, such precisely structured data is usually limited in quantity and in scope. Therefore, to enhance the quality of the embeddings it is important to consider more widely available medical information such as medical entity descriptions. In this paper we propose a novel framework, called Deep Patient Similarity (DeepPS). Specifically, DeepPS incorporates medical entity descriptions by augmenting the embeddings of medical entities and relations with the embeddings of words, which leverages both information from medical knowledge graph structures and the contexts of medical entity descriptions. Furthermore, DeepPS employs the embeddings to patient similarity learning by leveraging Siamese Convolutional Neural Network (CNN) with Spatial Pyramid Pooling (SPP). Extensive experiments on real datasets are conducted to show superior performance of our proposed framework.

Abstract licence: CC BY-NC-ND

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.