TY - GEN
T1 - Graph Attention Network and Dynamic Adjustment Mechanism for Drug Recommendation
AU - Lai, Xionghui
AU - Luo, Wuman
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Drug recommendation is an important part of healthcare. Leveraging electronic health records for drug recommendation can assist doctors to make better decisions. While deep learning has made some progress in drug recommendation, further efforts are needed to improve their accuracy and safety. The research of drug combination recommendation involves critical works such as representation of drug combination, safety of drug combination and so on. For representation of drug combination, traditional drug recommendation methods overlook the importance of drug co-occurrence among different historical drug combinations. This work uses graph attention network to calculate the different weight of drug combination co-occurrence. For safety of drug combination, this work designs a mechanism to dynamically adjust the recommendation strategy for balancing accuracy and security. The experimental results clearly show that this method has demonstrated significant effectiveness in improving the accuracy and safety of recommendation.
AB - Drug recommendation is an important part of healthcare. Leveraging electronic health records for drug recommendation can assist doctors to make better decisions. While deep learning has made some progress in drug recommendation, further efforts are needed to improve their accuracy and safety. The research of drug combination recommendation involves critical works such as representation of drug combination, safety of drug combination and so on. For representation of drug combination, traditional drug recommendation methods overlook the importance of drug co-occurrence among different historical drug combinations. This work uses graph attention network to calculate the different weight of drug combination co-occurrence. For safety of drug combination, this work designs a mechanism to dynamically adjust the recommendation strategy for balancing accuracy and security. The experimental results clearly show that this method has demonstrated significant effectiveness in improving the accuracy and safety of recommendation.
KW - Drug Drug Interactions
KW - Drug Recommendation
KW - Electronic Health Records
KW - Graph Attention Network
UR - https://www.scopus.com/pages/publications/105011825521
U2 - 10.1007/978-981-95-0027-7_8
DO - 10.1007/978-981-95-0027-7_8
M3 - Conference contribution
AN - SCOPUS:105011825521
SN - 9789819500260
T3 - Lecture Notes in Computer Science
SP - 80
EP - 91
BT - Advanced Intelligent Computing Technology and Applications - 21st International Conference, ICIC 2025, Proceedings
A2 - Huang, De-Shuang
A2 - Pan, Yijie
A2 - Chen, Wei
A2 - Li, Bo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Intelligent Computing, ICIC 2025
Y2 - 26 July 2025 through 29 July 2025
ER -