Name
Abstract | ||
|---|---|---|
Recently, drug recommendation tasks have been widely accepted in intelligent healthcare. Most of the existing methods utilize patients' electronic health records (EHRs) to achieve medical prediction. However, existing algorithms neglect the description of the patient's health status, which makes it difficult to adapt to the dynamic patients' condition. And they ignore the intrinsic encoding of drug molecular structure, resulting in the weak performance of drug recommendation. To fill the gap, we propose a molecular graph encoder for drug recommendation named MGEDR to capture the genuine health status of patients. Furthermore, We encode the drug molecular graph and functional groups separately to obtain subtle drug representation. And we design the degree encoder and functional groups encoder to seize the intrinsic features of the molecule efficaciously. Our experimental results show that our proposed MGEDR framework performs significantly better than state-of-the-art baseline methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-17189-5_8 | NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2022, PT II |
Keywords | DocType | Volume |
Molecular graph encoder, Drug recommendation, Graph neural network | Conference | 13552 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaiyuan Shi | 1 | 0 | 0.68 |
| Shaowu Zhang | 2 | 1 | 1.36 |
| Haifeng Liu | 3 | 0 | 0.68 |
| Yijia Zhang | 4 | 0 | 0.68 |
| Hongfei Lin | 5 | 0 | 2.37 |