TY - GEN
T1 - Diagnosis Ranking with Knowledge Graph Convolutional Networks
AU - Liu, Bing
AU - Zuccon, Guido
AU - Hua, Wen
AU - Chen, Weitong
N1 - Funding Information:
Acknowledgements. This research is supported by the Shenyang Science and Technology Plan Fund (No. 20-201-4-10), the Member Program of Neusoft Research of Intelligent Healthcare Technology, Co. Ltd. (No. NRMP001901)). A/Prof Guido Zuc-con is the recipient of an Australian Research Council DECRA Research Fellowship (DE180101579) and a Google Faculty Award.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/3
Y1 - 2021/3
N2 - The automatic diagnosis of a medical condition provided the symptoms exhibited by a patient is at the basis of systems for clinical decision support, as well as for applications such as symptom checkers. Existing methods have not fully exploited medical knowledge: this likely hinders their effectiveness. In this work, we propose a knowledge-aware diagnosis ranking framework based on medical knowledge graph (KG) and graph convolutional neural network (GCN). The medical KG is used to model hierarchy and causality relationships between diseases and symptoms. We have evaluated our proposed method using realistic patient cases. The empirical results show that our knowledge-aware diagnosis ranking framework can improve the effectiveness of medical diagnosis.
AB - The automatic diagnosis of a medical condition provided the symptoms exhibited by a patient is at the basis of systems for clinical decision support, as well as for applications such as symptom checkers. Existing methods have not fully exploited medical knowledge: this likely hinders their effectiveness. In this work, we propose a knowledge-aware diagnosis ranking framework based on medical knowledge graph (KG) and graph convolutional neural network (GCN). The medical KG is used to model hierarchy and causality relationships between diseases and symptoms. We have evaluated our proposed method using realistic patient cases. The empirical results show that our knowledge-aware diagnosis ranking framework can improve the effectiveness of medical diagnosis.
KW - Diagnosis ranking
KW - Graph Convolutional Networks
KW - Knowledge graph
UR - http://www.scopus.com/inward/record.url?scp=85107324705&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72113-8_24
DO - 10.1007/978-3-030-72113-8_24
M3 - Conference article published in proceeding or book
AN - SCOPUS:85107324705
SN - 9783030721121
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 359
EP - 374
BT - Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, Proceedings
A2 - Hiemstra, Djoerd
A2 - Moens, Marie-Francine
A2 - Mothe, Josiane
A2 - Perego, Raffaele
A2 - Potthast, Martin
A2 - Sebastiani, Fabrizio
PB - Springer Science and Business Media Deutschland GmbH
T2 - 43rd European Conference on Information Retrieval Research, ECIR 2021
Y2 - 28 March 2021 through 1 April 2021
ER -