A One-Size-Fits-Three Representation Learning Framework for Patient Similarity Search.

DOI码：

10.1007/s41019-023-00216-9

所属单位：

School of Informatics, Xiamen University,

发表刊物：

Data Science and Engineering 2023

刊物所在地：

China

关键字：

Patient similarity search · Multi-modal EHRs · Medical concepts · External knowledge · Graph representation learning

摘要：

Patient similarity search is an essential task in healthcare. Recent studies adopted electronic health records (EHRs) to learn patient representations for measuring the clinical similarities. These methods outperformed traditional methods, by capturing more information from various sources consisting of multi-modal EHRs, external knowledge and correlations among medical concepts. They often concerned certain type of data without taking full advantage of various information. We propose a graph representation learning framework, denoted by One-Size-Fits-Three (OSFT), that takes into account fusion-attention, neighbor-attention and global-attention from three types of information. Extensive experiments are conducted on two real datasets of MIMIC-III and MIMIC-IV, and the results verified the effectiveness and generality of our framework. When compared with baselines on patient similarity search, our framework achieved good effectiveness and comparative efficiency. The results provide new insights about whether the use of various information can better measure the patient similarity. The source codes are available at https://github.com/emmali808/ADDS/tree/master/EHRDeepHelper.

第一作者：

Yefan Huang

通讯作者：

Xiaoli Wang

合写作者：

Feng Luo,Zhu Di,Bohan Li, Bin Luo

论文类型：

Original Research

文献类型：

Journal Article

卷号：

8

期号：

3

页面范围：

306-317

是否译文：

否

发表时间：

2023-08-12