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Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation

Authors:

Xianli ZhangAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 2169 - 2178

https://doi.org/10.1145/3511808.3557380

Published: 17 October 2022 Publication History

Abstract

Predicting drug combinations according to patients' electronic health records is an essential task in intelligent healthcare systems, which can assist clinicians in ordering safe and effective prescriptions. However, existing work either missed/underutilized the important information lying in the drug molecule structure in drug encoding or has insufficient control over Drug-Drug Interactions (DDIs) rates within the predictions. To address these limitations, we propose CSEDrug, which enhances the drug encoding and DDIs controlling by leveraging multi-faceted drug knowledge, including molecule structures of drugs, Synergistic DDIs (SDDIs), and Antagonistic DDIs (ADDIs). We integrate these types of knowledge into CSEDrug by a graph-based drug encoder and multiple loss functions, including a novel triplet learning loss and a comprehensive DDI controllable loss. We evaluate the performance of CSEDrug in terms of accuracy, effectiveness, and safety on the public MIMIC-III dataset. The experimental results demonstrate that CSEDrug outperforms several state-of-the-art methods and achieves a 2.93% and a 2.77% increase in the Jaccard similarity scores and F1 scores, meanwhile, a 0.68% reduction of the ADDI rate (safer drug combinations), and 0.69% improvement of the SDDI rate (more effective drug combinations).

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Cited By

He KMao RHuang YGong TLi CCambria E(2024)Template-Free Prompting for Few-Shot Named Entity Recognition via Semantic-Enhanced Contrastive LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.331480735:12(18357-18369)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3314807
Zhu QHan FYang HLiu JHu XWang B(2024)RASNet: Recurrent aggregation neural network for safe and efficient drug recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112055299(112055)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112055
Mi JZu YWang ZHe J(2024)ACDNetJournal of Biomedical Informatics10.1016/j.jbi.2023.104570149:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.jbi.2023.104570
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Index Terms

Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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The Innovative Research Group of the National Natural Science Foundation of China
The National Natural Science Foundation of China under Grant
The Key Research and Development Program of Ningxia Hui Nationality Autonomous Region

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CIKM '22

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CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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Cited By

He KMao RHuang YGong TLi CCambria E(2024)Template-Free Prompting for Few-Shot Named Entity Recognition via Semantic-Enhanced Contrastive LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.331480735:12(18357-18369)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3314807
Zhu QHan FYang HLiu JHu XWang B(2024)RASNet: Recurrent aggregation neural network for safe and efficient drug recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112055299(112055)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112055
Mi JZu YWang ZHe J(2024)ACDNetJournal of Biomedical Informatics10.1016/j.jbi.2023.104570149:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.jbi.2023.104570
Wu JYu XHe KGao ZGong T(2024)PROMISE: A pre-trained knowledge-infused multimodal representation learning framework for medication recommendationInformation Processing & Management10.1016/j.ipm.2024.10375861:4(103758)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103758
Anbazhagan ESophiya EPrasanna Kumar R(2024)Sentiment-aware drug recommendations with a focus on symptom-condition mappingInternational Journal of Information Technology10.1007/s41870-024-02091-716:8(5195-5212)Online publication date: 19-Aug-2024
https://doi.org/10.1007/s41870-024-02091-7
Zhang YXu YZhao JDu TLi DZhao XWang JLi CTu JQi K(2023)An Automated Method of 3D Facial Soft Tissue Landmark Prediction Based on Object Detection and Deep LearningDiagnostics10.3390/diagnostics1311185313:11(1853)Online publication date: 25-May-2023
https://doi.org/10.3390/diagnostics13111853
He KMao RGong TCambria ELi C(2022)JCBIE: a joint continual learning neural network for biomedical information extractionBMC Bioinformatics10.1186/s12859-022-05096-w23:1Online publication date: 19-Dec-2022
https://doi.org/10.1186/s12859-022-05096-w
Mao BJia CHuang YHe KWu JGong TLi C(2022)Uncertainty-guided Mutual Consistency Training for Semi-supervised Biomedical Relation Extraction2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995416(2318-2325)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995416

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