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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (9): 113-124    DOI: 10.11925/infotech.2096-3467.2021.1452
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Drug Recommendation Based on Graph Neural Network with Patient Signs and Medication Data
Cheng Quan(),She Dexin
School of Economics and Management, Fuzhou University, Fuzhou 350108, China
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Abstract  

[Objective] This paper proposes a new drug recommendation algorithm based on the graph neural network integrating patient signs and medication history, aiming to improve the illness diagnosis and treatments. [Methods] First, we constructed a transitive relationship model for abnormal signs and drugs based on the Graph Neural Network(GNN). Then, we designed a precise drug recommendation plan with sign perception and built a heterogeneous graph for the “sign-patient-drug” relationship. Third, our model learned the node representation with sign perception using the R-GCN encoder. Finally, we designed a sign-aware interaction decoder, which integrated the abnormal signs to recommend drugs accurately. [Results] We examined the proposed model with diagnosis and treatment records of three types of diseases from the MIMIC-Ⅲ dataset. Compared with the SVD, NeuMF and NGCF models, the proposed method’s Recall@20 value increased by 5.76, 5.33 and 0.91 percentage point, respectively. Meanwhile, it increased the NDCG@20 value by 5.03, 4.25 and 2.67 percentage point. [Limitations] Our method did not include the dynamic changes of patients’ drug use due to the developments of diseases. [Conclusions] The proposed drug recommendation method is effective and feasible. This model could perceive the impacts of patient signs on medication, which lays foundations for precise drug recommendation algorithm integrating multi-dimensional information.

Key wordsSmart Medical      Graph Neural Network      Deep Learning      Precise Drug Recommendation     
Received: 25 December 2021      Published: 26 October 2022
ZTFLH:  TP39  
Fund:National Social Science Fund of China(19BTQ072)
Corresponding Authors: Cheng Quan,ORCID:0000-0002-7302-4527     E-mail: chengquan@fzu.edu.cn

Cite this article:

Cheng Quan, She Dexin. Drug Recommendation Based on Graph Neural Network with Patient Signs and Medication Data. Data Analysis and Knowledge Discovery, 2022, 6(9): 113-124.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1452     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I9/113

Heterogeneous Graph of “Signs-Patients-Drugs”
The Design of Sign-aware Drug Recommendation Model
Drug Recommendation Model Based on Physical Sign-Aware
The Updating Rule for Different Types of Nodes
病案ID 药物名称
143045 Atropine Sulfate
143045 Zolpidem Tartrate
143045 Midazolam HCl
129635 Clopidogrel Bisulfate
129635 Humulin-R Insulin
Patient Medication Record
疾病名称 病患数量
先天性冠状动脉粥样硬化 2 819
败血症 1 729
急性呼吸衰竭 935
Statistics of Different Disease
病案ID
HADM_ID
检查项ID
ITEMID
检查项
LABITEM
日期
DATE
检查状态
FALG
143045 50808 Free Calcium 2167-01-10 abnormal
143045 51221 Hematocrit 2167-01-10 abnormal
143045 51265 Platelet Count 2167-01-10 abnormal
143045 51301 White Blood Cells 2167-01-10 abnormal
Example of Patient Sign Data
病案ID 病患体征异常项集合
143045 51493, 51301, 51279, 51275, 51274, 51221, 50931
164982 50902, 50893, 50882, 50820, 51301, 51279, 51265, 51249, 51222, 51221, 50821, 50809, 50818, 51274, 51237, 50822, 50811, 50808
122973 51493, 50813, 51301, 51279, 51277, 51274, 51255, 51251, 51244, 51237, 51222, 51221, 51006, 50983, 50970, 50956, 50931, 50912, 50910, 50893, 50885, 50882, 50878, 50863, 50862, 50861, 51362
Patient’s Abnormal Physical Sign Data
Indicator Analysis of SA-GCN
模型 AUC Recall@10 NDCG@10 Recall@20 NDCG@20
SA-GCN 0.925 4 0.356 4 0.305 7 0.484 6 0.351 8
SA-GCN-1 0.921 7 0.337 8 0.276 9 0.467 3 0.313 7
SA-GCN-2 0.923 7 0.348 5 0.283 2 0.479 0 0.331 6
The Performance of Ablation Experiment
The Comparison of Node Represents
指标 SA-GCN SVD NeuMF NGCF
AUC 0.925 4 0.914 1 0.917 5 0.922 7
AUC Comparison on Dataset
模型 Recall@10 NDCG@10 Recall@20 NDCG@20
SA-GCN 0.356 4 0.305 7 0.484 6 0.351 7
SVD 0.302 6 0.256 9 0.427 0 0.301 4
NeuMF 0.327 1 0.275 0 0.431 3 0.309 2
NGCF 0.351 2 0.280 2 0.475 5 0.325 0
The Performance of TopN Recommendation
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