Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine
Zuoxi Yang
Abstract
As for many complex diseases, there is no "one size fits all" solutions for patients with a particular diagnosis in practice, which should be treated depends on patient's genetic, environmental, lifestyle choices and so on. Precision medicine can provide personalized treatment for a particular patient that has been drawn more and more attention. There are a large number of treatment options, which is overwhelming for clinicians to make best treatment for a particular patient. One of the effective ways to alleviate this problem is biomedical information retrieval system, which can automatically find out relevant information and proper treatment from mass of alternative treatments and cases. However, in the biomedical literature and clinical trials, there is a larger number of synonymous, polysemous and context terms, causing the semantic gap between query and document in traditional biomedical information retrieval systems.