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dc.contributor.authorGong, F
dc.contributor.authorWang, M
dc.contributor.authorWang, H
dc.contributor.authorWang, S
dc.contributor.authorLiu, M
dc.date.accessioned2021-02-08T22:30:04Z
dc.date.available2021-02-08T22:30:04Z
dc.date.issued2021
dc.identifier.issn2214-5796en_US
dc.identifier.doi10.1016/j.bdr.2020.100174en_US
dc.identifier.urihttp://hdl.handle.net/10072/401847
dc.description.abstractMost of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.en_US
dc.description.peerreviewedYesen_US
dc.languageenen_US
dc.publisherElsevier BVen_US
dc.relation.ispartofjournalBig Data Researchen_US
dc.relation.ispartofvolume23en_US
dc.subject.fieldofresearchStatisticsen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processingen_US
dc.subject.fieldofresearchInformation Systemsen_US
dc.subject.fieldofresearchcode0104en_US
dc.subject.fieldofresearchcode0801en_US
dc.subject.fieldofresearchcode0806en_US
dc.titleSMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendationen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationGong, F; Wang, M; Wang, H; Wang, S; Liu, M, SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation, Big Data Research, 2021, 23en_US
dc.date.updated2021-02-08T04:20:51Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Sen


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