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short-paper

ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction

Authors:

Chunyuan Zheng,

Xiao-Hua ZhouAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 682 - 687

https://doi.org/10.1145/3604915.3608813

Published: 14 September 2023 Publication History

Abstract

Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety. Recently, many studies have been devoted to effectively predict the drug-ADRs incidence rates. However, these methods either did not effectively utilize non-clinical data, i.e., physical, chemical, and biological information about the drug, or did little to establish a link between content-based and pure collaborative filtering during the training phase. In this paper, we first formulate the prediction of multi-label ADRs as a drug-ADR collaborative filtering problem, and to the best of our knowledge, this is the first work to provide extensive benchmark results of previous collaborative filtering methods on two large publicly available clinical datasets. Then, by exploiting the easy accessible drug characteristics from non-clinical data, we propose ADRNet, a generalized collaborative filtering framework combining clinical and non-clinical data for drug-ADR prediction. Specifically, ADRNet has a shallow collaborative filtering module and a deep drug representation module, which can exploit the high-dimensional drug descriptors to further guide the learning of low-dimensional ADR latent embeddings, which incorporates both the benefits of collaborative filtering and representation learning. Extensive experiments are conducted on two publicly available real-world drug-ADR clinical datasets and two non-clinical datasets to demonstrate the accuracy and efficiency of the proposed ADRNet. The code is available at https://github.com/haoxuanli-pku/ADRnet.

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Cited By

Li HWu KZheng CXiao YWang HGeng ZFeng FHe XWu POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Removing hidden confounding in recommendationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668502(54614-54626)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668502

Index Terms

ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 ACM.

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Published: 14 September 2023

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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Cited By

Li HWu KZheng CXiao YWang HGeng ZFeng FHe XWu POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Removing hidden confounding in recommendationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668502(54614-54626)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668502

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