Linking Physicians to Medical Research Results via Knowledge Graph Embeddings and Twitter

被引：0

作者：

Sadeghi, Afshin ^{[1
,2
,3
]}

Lehmann, Jens ^{[1
,2
,3
]}

机构：

[1] Univ Bonn, Smart Data Analyt Grp SDA, Bonn, Germany

[2] Fraunhofer Inst Intelligent Anal & Informat Syst, St Augustin, Germany

[3] Fraunhofer Inst Intelligent Anal & Informat Syst, Dresden, Germany

来源：

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2019, PT I | 2020年 / 1167卷

关键词：

Knowledge graph embeddings; Social media; Social good; Health care; Twitter; Machine learning;

D O I：

10.1007/978-3-030-43823-4_49

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Informing professionals about the latest research results in their field is a particularly important task in the field of health care, since any development in this field directly improves the health status of the patients. Meanwhile, social media is an infrastructure that allows public instant sharing of information, thus it has recently become popular in medical applications. In this study, we apply Multiple Distance Knowledge Graph Embeddings (MDE) to link physicians and surgeons to the latest medical breakthroughs that are shared as the research results on Twitter. Our study shows that using this method physicians can be informed about the new findings in their field given that they have an account dedicated to their profession.

引用

页码：622 / 630

页数：9

共 18 条

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