DMRNet: Effective Network for Accurate Discharge Medication Recommendation

Jiyun Shi, Yuqiao Wang, Chi Zhang, Zhaojing Luo*, Chengliang Chai, Meihui Zhang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Electronic Health Records, which contain abundant structured data information of the patients, can help clinicians and data scientists address complex medical issues, particularly medication recommendation. The recommendation of medications is crucial for accurate and timely prescriptions. It is a nuanced task that entails analyzing various sources of healthcare data. Traditional medication recommendation is performed manually, which is labor-intensive and error-prone. The development of Electronic Health Records enables automatic medication recommendation. There are mainly two categories of methods for automatic medication recommendation. The first category uses the patients' current visit information and the drug-drug interactions (DDI). For these methods, both the comprehensive patient's medical history and the significant medication-diagnosis knowledge are not exploited appropriately. The second category utilizes longitudinal patient data, but different history visits are incorporated indiscriminately. Furthermore, in clinical practice, the associations between historical medications and future prescriptions are highlighted. However, they are less emphasized in current methods. Nevertheless, this is less emphasized by current automatic medication recommendation methods. To tackle the above challenges, we propose a three-module Discharge Medication Recommendation Network, called DMRNet, for accurate discharge medication recommendations. Specifically, the Information Integration Module combines information from the current visit and significant external knowledge e.g., the Diagnosis-Medication Co-occurrence (DMC) relationship. The Medication Retention Module is specially designed to capture the associations between the historical medications and the recommended medications. The History Retrieval Module differentiates the significance of different historical visits and incorporates them based on different significance values. Experimental evaluations on benchmark datasets, i.e., MIMIC-III and MIMIC-IV, confirm DMRNet's superiority over state-of-the-art baseline methods in terms of Jaccard Similarity, F1-score, Precision and Recall.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024
PublisherIEEE Computer Society
Pages3393-3406
Number of pages14
ISBN (Electronic)9798350317152
DOIs
Publication statusPublished - 2024
Event40th IEEE International Conference on Data Engineering, ICDE 2024 - Utrecht, Netherlands
Duration: 13 May 202417 May 2024

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627
ISSN (Electronic)2375-0286

Conference

Conference40th IEEE International Conference on Data Engineering, ICDE 2024
Country/TerritoryNetherlands
CityUtrecht
Period13/05/2417/05/24

Keywords

  • Deep learning
  • Electronic health records
  • Healthcare analytics
  • Interpretability
  • Medication recommendation

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