Deep Learning Model of Drug Recommendation Based on Patient Similarity Analysis

被引：0

作者：

Jialun W. ^{[1
]}

Ruonan Z. ^{[2
]}

Wulin K. ^{[3
]}

Puwei Y. ^{[3
]}

机构：

[1] School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an

[2] Library of Xi’an Jiaotong University, Xi’an

[3] Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang

来源：

Data Analysis and Knowledge Discovery | 2023年 / 7卷 / 06期

关键词：

Deep Learning; Drug Recommendation; Electronic Health Records; Patient Representation Learning;

D O I：

10.11925/infotech.2096-3467.2022.0535

中图分类号：

学科分类号：

摘要：

[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. © 2023 Chinese Medical Association. All rights reserved.

引用

页码：148 / 160

页数：12

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