EDRMM: enhancing drug recommendation via multi-granularity and multi-attribute representation

DOI number:

10.1186/s12859-025-06167-4

Affiliation of Author(s):

School of Informatics, Xiamen University,

Journal:

BMC Bioinformatics

Place of Publication:

United Kingdom

Key Words:

"Drug recommendation";"Electronic Health Record";"Representation learning"

Abstract:

Background:: Drug recommendation is a crucial application of artificial intelligence in medical practice. Although many models have been proposed to solve this task, two challenges remain unresolved: (i) most existing models use all historical visits as input, overlooking fine-grained correlations between historical and current information; (ii) Electronic Health Records (EHRs) are underutilized, with only partial information considered to describe patient conditions. To tackle the challenges, we propose a novel drug recommendation model, denoted by EDRMM, which incorporates multi-granularity and multi-attribute information into representation learning. We develop a longitudinal attribute-level history selection mechanism to effectively identify fine-grained historical information that is highly relevant to a patient’s current clinical conditions. We analyze the impact of key Electronic Health Record (EHR) attributes, demonstrating that incorporating such attributes into patient representations can further boost performance. We also design an adaptive global Drug–Drug Interaction (DDI) risk regularization term for the DDI loss function to better balance accuracy and safety during training. Results:: Experimental results show that our model achieves state-of-the-art performance on a widely used MIMIC-III dataset. Conclusions:: EDRMM overcomes two key drug recommendation limitations through three innovations: (1) Dynamic attribute-level history selection, which retrieves relevant features and filters out noise, (2) The integration of multi-attribute EHR with attribute-specific encoding strategies to generate comprehensive patient representations, and (3) Hybrid optimization balancing accuracy and safety via adaptive DDI regularization. The combination of these three innovations enables the proposed EDRMM to achieve the best recommendation performance on the MIMIC-III dataset.

First Author:

Feiyan Liu

Correspondence Author:

Xiaoli Wang

Co-author:

Wenhao Wang, Jiawei Zheng, Yibo Xie, Xiaoli Wang, Dongxiang Zhang

Indexed by:

Original Research

Discipline:

生物信息学 / 计算生物学 / 医疗人工智能 (AI in medicine)

First-Level Discipline:

信息与计算科学 / 计算机科学 / 医学信息学

Document Type:

Journal Article

Volume:

26

Issue:

1

ISSN No.:

1471-2105

Translation or Not:

no

Date of Publication:

2025-07-10