TY - JOUR
T1 - Medical-Knowledge-Based Graph Neural Network for Medication Combination Prediction
AU - Gao, Chao
AU - Yin, Shu
AU - Wang, Haiqiang
AU - Wang, Zhen
AU - Du, Zhanwei
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Heuristic medication features
KW - medical knowledge
KW - medication combination prediction (MCP)
KW - patient representation
UR - http://www.scopus.com/inward/record.url?scp=85159804258&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2023.3266490
DO - 10.1109/TNNLS.2023.3266490
M3 - 文章
C2 - 37141055
AN - SCOPUS:85159804258
SN - 2162-237X
VL - 35
SP - 13246
EP - 13257
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 10
ER -