research-article

4SDrug: Symptom-based Set-to-set Small and Safe Drug Recommendation

Authors:

Vicki S. Hertzberg,

Carl YangAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3970 - 3980

https://doi.org/10.1145/3534678.3539089

Published: 14 August 2022 Publication History

Abstract

Drug recommendation is an important task of AI for healthcare. To recommend proper drugs, existing methods rely on various clinical records (e.g., diagnosis and procedures), which are commonly found in data such as electronic health records (EHRs). However, detailed records as such are often not available and the inputs might merely include a set of symptoms provided by doctors. Moreover, existing drug recommender systems usually treat drugs as individual items, ignoring the unique requirements that drug recommendation has to be done on a set of items (drugs), which should be as small as possible and safe without harmful drug-drug interactions (DDIs).

To deal with the challenges above, in this paper, we propose a novel framework of Symptom-based Set-to-set Small and Safe drug recommendation (4SDrug). To enable set-to-set comparison, we design set-oriented representation and similarity measurement for both symptoms and drugs. Further, towards the symptom sets, we devise importance-based set aggregation to enhance the accuracy of symptom set representation; towards the drug sets, we devise intersection-based set augmentation to ensure smaller drug sets, and apply knowledge-based and data-driven penalties to ensure safer drug sets. Extensive experiments on two real-world EHR datasets, i.e., the public benchmark one of MIMIC-III and the industrial large-scale one of NELL, show drastic performance gains brought by 4SDrug, which outperforms all baselines in most effectiveness measures, while yielding the smallest sets of recommended drugs and 26.83% DDI rate reduction from the ground-truth data.

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Cited By

Zhao ZJing YFeng FWu JGao CHe XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease PatientsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657785(533-542)Online publication date: 10-Jul-2024
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Lin ZYan QLiu WWang SWang MTan YYang C(2024)Automatic Hypergraph Generation for Enhancing Recommendation With Sparse OptimizationIEEE Transactions on Multimedia10.1109/TMM.2023.333808326(5680-5693)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3338083
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Index Terms

4SDrug: Symptom-based Set-to-set Small and Safe Drug Recommendation
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

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Published: 14 August 2022

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Presentation Video https://dl.acm.org/doi/10.1145/3534678.3539089#KDD22-fp1121.mp4

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the National Natural Science Foundation of China

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

Washington DC, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Zhao ZJing YFeng FWu JGao CHe XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease PatientsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657785(533-542)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657785
Lin ZYan QLiu WWang SWang MTan YYang C(2024)Automatic Hypergraph Generation for Enhancing Recommendation With Sparse OptimizationIEEE Transactions on Multimedia10.1109/TMM.2023.333808326(5680-5693)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3338083
Zhu FZhang XZhang BXu YCui L(2024)Medicine Package Recommendation via Dual-Level Interaction Aware Heterogeneous GraphIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.336155228:4(2294-2303)Online publication date: Apr-2024
https://doi.org/10.1109/JBHI.2024.3361552
Shi JWang YZhang CLuo ZChai CZhang M(2024)DMRNet: Effective Network for Accurate Discharge Medication Recommendation2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00262(3393-3406)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00262
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
Bhattacharjee SSaha BSaha S(2024)Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniquesComputers in Biology and Medicine10.1016/j.compbiomed.2024.108413174:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108413
Tan WWang WZhou XBuntine WBingham GYin H(2024)OntoMedRec: Logically-pretrained model-agnostic ontology encoders for medication recommendationWorld Wide Web10.1007/s11280-024-01268-127:3Online publication date: 23-Apr-2024
https://dl.acm.org/doi/10.1007/s11280-024-01268-1
Wu ZYao HLiebovitz DSun JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)An iterative self-learning framework for medical domain generalizationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668515(54833-54854)Online publication date: 10-Dec-2023
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Yang NZeng KWu QYan J(2023)MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation LearningProceedings of the ACM Web Conference 202310.1145/3543507.3583872(4075-4085)Online publication date: 30-Apr-2023
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