MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs
Kai Lei, Kaiqi Yuan, Qiang Zhang, Ying Shen
TL;DR
MedSim is a new semantic similarity measure leveraging biomedical knowledge graphs and corpus data, improving antibiotic substitution decisions and aiding drug safety applications.
Contribution
It introduces MedSim, a novel similarity method that combines hierarchical, corpus, and feature vector data from biomedical knowledge graphs.
Findings
MedSim outperforms existing similarity methods statistically.
Applied successfully to antibiotic substitution and drug abuse prevention.
Evaluated on 528 antibiotic pairs scored by doctors.
Abstract
We present MedSim, a novel semantic SIMilarity method based on public well-established bio-MEDical knowledge graphs (KGs) and large-scale corpus, to study the therapeutic substitution of antibiotics. Besides hierarchy and corpus of KGs, MedSim further interprets medicine characteristics by constructing multi-dimensional medicine-specific feature vectors. Dataset of 528 antibiotic pairs scored by doctors is applied for evaluation and MedSim has produced statistically significant improvement over other semantic similarity methods. Furthermore, some promising applications of MedSim in drug substitution and drug abuse prevention are presented in case study.
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Machine Learning in Bioinformatics · Computational Drug Discovery Methods