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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (6): 148-160    DOI: 10.11925/infotech.2096-3467.2022.0535
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Deep Learning Model of Drug Recommendation Based on Patient Similarity Analysis
Wu Jialun1,Zhang Ruonan2,Kang Wulin3,Yuan Puwei3()
1School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
2Library of Xi’an Jiaotong University, Xi’an 710049, China
3Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712046, China
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Abstract  

[Objective] This paper develops a deep learning model that accurately predicts drug combinations by analyzing structured time-series medical data and patient similarity. [Methods] Our model learned comprehensive patient representations by parsing structured time-series data through two attention mechanisms. Then, we calculated the patients’ similarity to enrich their representation and transformed the drug recommendation problem into a multi-label learning task. [Results] We examined the new model with the MIMIC-III dataset. Compared to other mainstream models, the proposed one achieved improvements of at least 1.09%, 2.38%, 1.40%, and 1.08% in DDI rate, Jaccard similarity, PRAUC, and F1-score, respectively. [Limitations] Our model should have included the prior domain knowledge from biomedical fields. More research is needed to thoroughly investigate the noise in the data and potential issues in clinical applications. [Conclusions] The proposed method can learn comprehensive patient representations and enhance the safety and accuracy of drug recommendation tasks.
Key words： Drug Recommendation      Electronic Health Records      Patient Representation Learning      Deep Learning     
Received: 25 May 2022      Published: 09 November 2022
ZTFLH:  TP391  
  G35  
Fund:2021 Key R&D Plan of Shaanxi Provincial Department of Science and Technology “Joint Key Projects of Colleges and Universities”(2021GXLH-Z-095);2021 Shaanxi Provincial Department of Education Scientific Research Projects “Local Special Services(21JC010);Shaanxi Provincial Department of Education “Shaanxi University Engineering Research Center for the Prevention and Treatment of Osteodegenerative Diseases with Integrated Traditional Chinese and Western Medicine”(陕教技办[2021]10号)
Corresponding Authors: Yuan Puwei，ORCID：0000-0001-7916-8823，E-mail：spine_surgeon@163.com。   
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Jialun Wu
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Cite this article:

Wu Jialun, Zhang Ruonan, Kang Wulin, Yuan Puwei. Deep Learning Model of Drug Recommendation Based on Patient Similarity Analysis. Data Analysis and Knowledge Discovery, 2023, 7(6): 148-160.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0535     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I6/148

Task Definition of Drug Recommendation
符号 定义
x i t 第 i个患者第 t次入院的医疗记录
d i t 第 i个患者第 t次入院的诊断记录
p i t 第 i个患者第 t次入院的手术记录
m i t 第 i个患者第 t次入院的用药记录
X i 第 i个患者的全部病史记录
X i 1 : t 第 i个患者从第一次到第 t次入院的病史记录
d e i t , p e i t , m e i t 医疗记录（诊断、手术、用药）的嵌入向量
α i ( t ) 第 i个患者第 t次入院的代码级（Code-Level）注意力
β i ( t ) 第 i个患者第 t次入院的访问级（Visit-Level）注意力
q i t 第 i个患者所有病史的整合表示
s i , j t , k 患者表示 q i t与 q j k之间的相似性
S n 相似病史序列集
m ^ i t 第 i个患者的预测药物组合
m i t 第 i个患者的真实药物组合
Notation
Detailed Architecture of MedSim Model
项目 数量
患者 6 350
临床事件 15 031
诊断 1 958
手术 1 430
药物（ATC 3） 132
药物（ATC 4） 266
访问的平均值/总数 2.37/29
访问诊断的平均值/总数 10.51/128
访问手术的平均值/总数 3.84/50
访问药物的平均值/总数（ATC 3） 11.18/64
访问药物的平均值/总数（ATC 4） 11.87/81
DDI种类数量 50
Statistics of the Dataset
代码 模型 DDI率 Jaccard相似性 F1值 PRAUC 药物平均数量
ATC3 LR 0.088 1±0.000 8 0.496 6±0.002 8 0.633 5±0.002 4 0.730 0±0.003 2 15.990 4±0.099 0
RETAIN 0.093 3±0.001 5 0.497 8±0.002 8 0.637 2±0.002 5 0.748 3±0.003 0 18.742 4±0.073 8
LEAP 0.094 5±0.000 4 0.480 4±0.002 6 0.621 8±0.002 6 0.746 3±0.003 0 18.742 4±0.073 8
DMNC 0.088 1±0.000 4 0.495 4±0.002 0 0.637 3±0.002 7 0.735 9±0.003 8 20.000 0±0.000 0
GAMENet 0.087 9±0.000 7 0.499 5±0.002 3 0.643 8±0.002 3 0.749 1±0.002 6 20.793 9±0.062 8
MedSim 0.076 1±0.000 9 0.511 8±0.002 3 0.657 9±0.002 3 0.756 3±0.003 0 17.456 0±0.068 6
ATC4 LR 0.089 5±0.000 7 0.382 6±0.002 4 0.531 3±0.002 3 0.627 1±0.001 7 14.332 5±0.112 8
RETAIN 0.099 2±0.001 7 0.392 0±0.003 7 0.553 5±0.004 1 0.622 7±0.004 7 16.631 9±0.163 2
LEAP 0.109 0±0.001 0 0.382 1±0.002 9 0.531 8±0.003 4 0.594 3±0.005 2 18.998 1±0.060 4
DMNC 0.103 6±0.000 4 0.394 3±0.002 1 0.543 4±0.002 3 0.619 4±0.003 0 20.000 0±0.000 0
GAMENet 0.079 4±0.000 8 0.391 7±0.003 6 0.552 5±0.003 8 0.618 3±0.004 1 19.349 1±0.104 1
MedSim 0.068 5±0.000 3 0.415 5±0.002 4 0.566 5±0.002 4 0.629 1±0.002 8 20.076 4±0.083 3
Performance Comparison
γ DDI率 Jaccard相似性 F1值 PRAUC 药物平均数量
0.00 0.010 8±0.000 3 0.409 7±0.002 1 0.560 2±0.002 3 0.612 0±0.002 9 19.059 9±0.057 9
0.01 0.022 5±0.000 4 0.409 9±0.002 6 0.561 1±0.002 6 0.610 9±0.002 5 19.140 3±0.079 7
0.02 0.024 3±0.000 3 0.411 0±0.002 2 0.561 7±0.002 2 0.617 9±0.002 5 19.114 6±0.093 1
0.03 0.037 0±0.000 2 0.413 1±0.002 2 0.564 2±0.002 3 0.622 1±0.002 5 19.352 0±0.113 7
0.04 0.040 7±0.000 3 0.413 6±0.001 8 0.565 0±0.001 8 0.627 9±0.002 2 19.827 2±0.105 8
0.05 0.054 4±0.000 2 0.414 5±0.001 9 0.566 1±0.001 9 0.628 0±0.002 8 20.165 8±0.090 5
0.06 0.068 5±0.000 3 0.415 5±0.002 4 0.566 5±0.002 4 0.629 1±0.002 8 20.076 4±0.083 3
0.07 0.074 4±0.000 4 0.418 0±0.002 2 0.568 7±0.002 1 0.633 3±0.002 3 20.555 7±0.098 2
0.08 0.081 5±0.000 4 0.419 3±0.002 8 0.590 3±0.002 8 0.636 4±0.002 6 20.890 8±0.120 7
Performance Under Acceptance DDI Rate γ
n DDI率 Jaccard相似性 F1值 PRAUC 药物平均数量
6 0.066 2±0.000 3 0.408 1±0.002 1 0.559 6±0.002 2 0.624 0±0.002 3 20.585 3±0.105 0
8 0.067 0±0.000 3 0.415 3±0.002 3 0.566 1±0.002 4 0.628 7±0.002 9 19.214 6±0.063 6
10 0.068 5±0.000 3 0.415 5±0.002 4 0.566 5±0.002 4 0.629 1±0.002 8 20.076 4±0.083 3
12 0.067 7±0.000 4 0.414 2±0.002 1 0.565 2±0.002 1 0.628 1±0.002 3 19.351 8±0.113 8
14 0.067 9±0.000 3 0.414 0±0.002 6 0.564 5±0.002 6 0.627 3±0.003 0 18.045 1±0.058 8
Performance Under Different Candidate Set
病例 模型 DDI率 药物组合推荐
病例1 处方 0.126 3 A01AD， A02BA， B05CX， C01CA， M01AB， N01AX， C07AB， N02BE， C03CA， N07AA， A02BC， C10AA， A06AD， A12BA， J01DB， C01DA， A01AB， A03FA， A02AA， N06A
LEAP 0.105 2 15 正确： A01AD， A02BA， B05CX， C01CA， M01AB， N01AX， C07AB， N02BE， C03CA， N07AA， A02BC， C10AA， A06AD， A12BA， J01DB
6 错误： A07AA， A02BX， N02AA， B01AB， C01BD（2）， J01DH
GAMENet 0.090 8 17 正确： A01AD， A02BA， B05CX， C01CA， M01AB， N01AX， C07AB， N02BE， C03CA， N07AA， A02BC， C10AA， A06AD， A12BA， J01DB， C01DA， A03FA
6 错误： A07AA， A02BX， N02AA， B01AB， H03AA， J01DH
MedSim 0.087 1 19 正确： A01AD， A02BA， B05CX， C01CA， M01AB， N01AX， C07AB， N02BE， C03CA， N07AA， A02BC， C10AA， A06AD， A12BA， J01DB， A01AB， A03FA， A02AA， N06AX
1 错误： A02B
病例2 处方 0.097 7 A07AA， N01AX， N02BE， B05CX， N03AX， A06AD， A12BA， A01AB，
B03BB， N07BA， A04AA， C01EB， D04AA， J01DD， N05CF， A01A
LEAP 0.085 7 8 正确： A07AA， B05CX， N03AX， A06AD， A12BA， A01AB， B03BB， N07BA
7 错误： A01AD（1）， A02BA， N02AA， B01AB， C02DB， A02BC， N03AB（2）
GAMENet 0.079 2 8 正确： A07AA， B05CX， N03AX， A06AD， A12BA， A01AB， B03BB， N07BA
7 错误： A01AD（1）， A02BA， N02AA， B01AB， C02DB， R06AX（1）， J01XX
MedSim 0.076 1 8 正确： A07AA， N02BE， B05CX， N03AX， A06AD， A12BA， B03BB， A04AA
6 错误： A02BA， N02AA， N06AX， B01AB， N05BA（1）， A02BC
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