A One-Size-Fits-Three Representation Learning Framework for Patient Similarity Search
Abstract 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...
Main Authors: | Yefan Huang, Feng Luo, Xiaoli Wang, Zhu Di, Bohan Li, Bin Luo |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2023-08-01
|
Series: | Data Science and Engineering |
Subjects: | |
Online Access: | https://doi.org/10.1007/s41019-023-00216-9 |
Similar Items
-
Similar Supergraph Search Based on Graph Edit Distance
by: Masataka Yamada, et al.
Published: (2021-07-01) -
Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities
by: Xing Li, et al.
Published: (2022-12-01) -
Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature
by: Giacomo Frisoni, et al.
Published: (2021-12-01) -
Statistical Analysis of Multisensory and Text-Derived Representations on Concept Learning
by: Yuwei Wang, et al.
Published: (2022-04-01) -
What makes representations good representations for science education? A teacher-oriented summary of significant findings and a practical guideline for the transfer into teaching
by: Tonyali Büşra, et al.
Published: (2023-07-01)