Title: Demographic Aware Probabilistic Medical Knowledge Graph Embeddings of Electronic Medical Records
Authors: Guluzade, Aynur
Kacupaj, Endri
Maleshkova, Maria 
Language: eng
Keywords: Demographics Probabilistic medical knowledge graph;Electronic medical record;Knowledge graph embedding
Issue Date: 22-Mar-2021
Publisher: Springer
Document Type: Book Part
Journal / Series / Working Paper (HSU): Lecture Notes in Computer Science
Page Start: 408
Page End: 417
Published in (Book): Artificial Intelligence in Medicine
Publisher Place: Cham
Conference: 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Virtual Event, June 15–18, 2021
Abstract: 
Medical knowledge graphs (KGs) constructed from Electronic Medical Records (EMR) contain abundant information about patients and medical entities. The utilization of KG embedding models on these data has proven to be efficient for different medical tasks. However, existing models do not properly incorporate patient demographics and most of them ignore the probabilistic features of the medical KG. In this paper, we propose DARLING (Demographic Aware pRobabiListic medIcal kNowledge embeddinG), a demographic-aware medical KG embedding framework that explicitly incorporates demographics in the medical entities space by associating patient demographics with a corresponding hyperplane. Our framework leverages the probabilistic features within the medical entities for learning their representations through demographic guidance. We evaluate DARLING through link prediction for treatments and medicines, on a medical KG constructed from EMR data, and illustrate its superior performance compared to existing KG embedding models.
Organization Units (connected with the publication): University of Bonn
ISBN: 9783030772109
ISSN: 03029743
Publisher DOI: 10.1007/978-3-030-77211-6_48
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