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

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

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  • (2024)ACDNetJournal of Biomedical Informatics10.1016/j.jbi.2023.104570149:COnline publication date: 17-Apr-2024
  • (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
  • (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
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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,
      • Li Xiong
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      Published: 17 October 2022

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      Author Tags

      1. deep learning
      2. drug recommendation
      3. electronic health records
      4. healthcare

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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 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
      Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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      Cited By

      View all
      • (2024)ACDNetJournal of Biomedical Informatics10.1016/j.jbi.2023.104570149:COnline publication date: 17-Apr-2024
      • (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
      • (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
      • (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
      • (2022)JCBIE: a joint continual learning neural network for biomedical information extractionBMC Bioinformatics10.1186/s12859-022-05096-w23:1Online publication date: 19-Dec-2022
      • (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

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