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Jan 25, 2024 · Transformation of patient data extracted from a database into fixed-length numerical vectors requires expertise in topical medical knowledge ...
Abstract. Transformation of patient data extracted from a database into fixed-length numerical vectors requires expertise in topical medical knowledge as ...
In this study, we propose a machine learning-based method to for this purpose applicable to electronic medical data recorded during hospitalization, which ...
Jan 11, 2024 · This study aimed to utilize an unsupervised graph representation learning method to transform the unstructured inpatient information from electronic medical ...
Transformation of patient data extracted from a database into fixed-length numerical vectors requires expertise in topical medical knowledge as well as data ...
The results showed that the LSTM-GNN outperformed the LSTM-only baseline on length of stay prediction tasks on the eICU database, indicating that exploiting ...
We propose a novel graph representation learning approach with a heterogeneous graph neural network to model structured electronic health records and formulate ...
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Graph Representation Learning-Based Fixed-Length Clinical Feature Vector Generation from Heterogeneous Medical Records. Transformation of patient data ...
Dec 26, 2023 · Summary. Objectives: Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs.
Dec 7, 2022 · Graph representation learning is a method for introducing how to effectively construct and learn patient embeddings using electronic medical records.
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