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

Autor: Yefan Huang, Feng Luo, Xiaoli Wang, Zhu Di, Bohan Li, Bin Luo
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Data Science and Engineering, Vol 8, Iss 3, Pp 306-317 (2023)
Druh dokumentu: article
ISSN: 2364-1185
2364-1541
DOI: 10.1007/s41019-023-00216-9
Popis: 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 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 .
Databáze: Directory of Open Access Journals
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