Medical-Knowledge-Based Graph Neural Network for Medication Combination Prediction

Chao Gao, Shu Yin, Haiqiang Wang, Zhen Wang, Zhanwei Du, Xuelong Li

科研成果: 期刊稿件文章同行评审

12 引用 (Scopus)

摘要

Medication combination prediction (MCP) can provide assistance for experts in the more thorough comprehension of complex mechanisms behind health and disease. Many recent studies focus on the patient representation from the historical medical records, but neglect the value of the medical knowledge, such as the prior knowledge and the medication knowledge. This article develops a medical-knowledge-based graph neural network (MK-GNN) model which incorporates the representation of patients and the medical knowledge into the neural network. More specifically, the features of patients are extracted from their medical records in different feature subspaces. Then these features are concatenated to obtain the feature representation of patients. The prior knowledge, which is calculated according to the mapping relationship between medications and diagnoses, provides heuristic medication features according to the diagnosis results. Such medication features can help the MK-GNN model learn optimal parameters. Moreover, the medication relationship in prescriptions is formulated as a drug network to integrate the medication knowledge into medication representation vectors. The results reveal the superior performance of the MK-GNN model compared with the state-of-the-art baselines on different evaluation metrics. The case study manifests the application potential of the MK-GNN model.

源语言英语
页(从-至)13246-13257
页数12
期刊IEEE Transactions on Neural Networks and Learning Systems
35
10
DOI
出版状态已出版 - 2024

指纹

探究 'Medical-Knowledge-Based Graph Neural Network for Medication Combination Prediction' 的科研主题。它们共同构成独一无二的指纹。

引用此