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FastRx: Exploring Fastformer and Memory-Augmented Graph Neural Networks for Personalized Medication Recommendations

Published: 14 December 2024 Publication History

Abstract

Personalized medication recommendations aim to suggest a set of medications based on the clinical conditions of a patient. Not only should the patient’s diagnosis, procedure, and medication history be considered, but drug-drug interactions (DDIs) must also be taken into account to prevent adverse drug reactions. Although recent studies on medication recommendation have considered DDIs and patient history, personalized disease progression and prescription have not been explicitly modeled. In this work, we proposed FastRx, a Fastformer-based medication recommendation model to capture longitudinality in patient history, in combination with Graph Convolutional Networks (GCNs) to handle DDIs and co-prescribed medications in Electronic Health Records (EHRs). Our extensive experiments on the MIMIC-III dataset demonstrated superior performance of the proposed FastRx over existing state-of-the-art models for medication recommendation. The source code and data used in the experiments are available at https://github.com/pnmthaoct/FastRx.

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  1. FastRx: Exploring Fastformer and Memory-Augmented Graph Neural Networks for Personalized Medication Recommendations

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      Published In

      cover image ACM Transactions on Intelligent Systems and Technology
      ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 6
      December 2024
      727 pages
      EISSN:2157-6912
      DOI:10.1145/3613712
      • Editor:
      • Huan Liu
      Issue’s Table of Contents

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 14 December 2024
      Online AM: 17 September 2024
      Accepted: 21 August 2024
      Revised: 12 August 2024
      Received: 03 March 2024
      Published in TIST Volume 15, Issue 6

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      Author Tags

      1. Medication Recommendation
      2. Electronic Health Records
      3. Graph Convolutional Networks
      4. Attention Mechanism

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