Faculty, Staff and Student Publications
Language
English
Publication Date
6-1-2024
Journal
Proceedings of the IEEE International Conference on Healthcare Informatics
DOI
10.1109/ichi61247.2024.00009
PMID
39698046
PMCID
PMC11654828
PubMedCentral® Posted Date
12-18-2024
PubMedCentral® Full Text Version
Author MSS
Abstract
Predictive analytics using Electronic Health Records (EHRs) have become an active research area in recent years, especially with the development of deep learning techniques. A popular EHR data analysis paradigm in deep learning is patient representation learning, which aims to learn a condensed mathematical representation of individual patients. However, EHR data are often inherently irregular, i.e., data entries were captured at different times as well as with different contents due to the individualized needs of each patient. Most of the work focused on the provision of deep neural networks with attention mechanisms that generate complete patient representations that can be readily used for downstream prediction tasks. However, such approaches fail to take patient similarity into account, which is generally used in clinical reasoning scenarios. This study presents a new Contrastive Graph Similarity Network for similarity calculation among patients in large EHR datasets. Particularly, we apply graph-based similarity analysis that explicitly extracts the clinical characteristics of each patient and aggregates the information of similar patients to generate rich patient representations. Experimental results on real-world EHR databases demonstrate the effectiveness and superiority of our method for the task of vital signs imputation and ICU patient deterioration prediction.
Keywords
Patient Similarity Calculation, Patient Representation Learning, Graph Contrastive Learning
Published Open-Access
yes
Recommended Citation
Liu, Yuxi; Zhang, Zhenhao; Qin, Shaowen; et al., "Fine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks" (2024). Faculty, Staff and Student Publications. 919.
https://digitalcommons.library.tmc.edu/uthshis_docs/919