An Intelligent Medical Analysis System Enhanced with Deep Graph Neural Networks

DOI码：

10.1145/3459637.3481966

所属单位：

School of Informatics, Xiamen University,

发表刊物：

CIKM 2021

关键字：

Medial Analysis; Graph Neural Networks; Medical Case Clustering; Medical Case Search

摘要：

This paper demonstrates an intelligent medical analysis system. We aim to address two main challenges: 1) medical data often contain heterogeneous information which are usually valuable but difficult to be modeled; 2) medical data are often lacking of large scale labeled data which usually require huge efforts to build. To resolve the first challenge, we propose a novel multi-modal heterogeneous graph model to represent the medical data. Based on this model, graph neural networks can be directly applied to effective medical case clustering. This helps to resolve the second challenge for label assignment in the same cluster. To further evaluate the practical use of the proposed model, the system also proposes an effective similar medical case retrieval framework based on a novel graph similarity learning model. We have implemented the system and the source codes are published at https://github.com/emmali808/ADDS. With our system, users can easily pinpoint valuable historical medical information they are interested in and obtain closely relevant medical cases for further diagnosis.

第一作者：

Feng Luo

通讯作者：

Xiaoli Wang

合写作者：

Yue Zhang

论文类型：

Original Research

文献类型：

Conference Paper

页面范围：

4754-4758

是否译文：

否

发表时间：

2021-11-01