research-article

Conditional Generation Net for Medication Recommendation

Authors:

Jiacheng Jiang,

Xian WuAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 935 - 945

https://doi.org/10.1145/3485447.3511936

Published: 25 April 2022 Publication History

Abstract

Medication recommendation targets to provide a proper set of medicines according to patients’ diagnoses, which is a critical task in clinics. Currently, the recommendation is manually conducted by doctors. However, for complicated cases, like patients with multiple diseases at the same time, it’s difficult to propose a considerate recommendation even for experienced doctors. This urges the emergence of automatic medication recommendation which can help treat the diagnosed diseases without causing harmful drug-drug interactions. Due to the clinical value, medication recommendation has attracted growing research interests. Existing works mainly formulate medication recommendation as a multi-label classification task to predict the set of medicines. In this paper, we propose the Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. Given a patient, the proposed model first retrieves his or her historical diagnoses and medication recommendations and mines their relationship with current diagnoses. Then in predicting each medicine, the proposed model decides whether to copy a medicine from previous recommendations or to predict a new one. This process is quite similar to the decision process of human doctors. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches.

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Conditional Generation Net for Medication Recommendation

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
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Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Zhu FCui LXu YQu ZShen Z(2024)A Survey of Personalized Medicine RecommendationInternational Journal of Crowd Science10.26599/IJCS.2023.91000138:2(77-82)Online publication date: May-2024
https://doi.org/10.26599/IJCS.2023.9100013
Hadizadeh Moghaddam ANayebi Kerdabadi MLiu MYao ZSerra ESpezzano F(2024)Contrastive Learning on Medical Intents for Sequential Prescription RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679836(748-757)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679836
Li XLiang SLei YLi CHou YZheng DMa TSerra ESpezzano F(2024)CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient Health StateProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679542(1276-1285)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679542
Tan JRong YZhao KBian TXu THuang JCheng HMeng HSerra ESpezzano F(2024)Natural Language-Assisted Multi-modal Medication RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679529(2200-2209)Online publication date: 21-Oct-2024
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Tang THan ZCai ZYu SZhou XOseni TDas S(2024)Personalized Federated Graph Learning on Non-IID Electronic Health RecordsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.337029735:9(11843-11856)Online publication date: Sep-2024
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