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Related Concept Videos

Drug Classes and Categories01:25

Drug Classes and Categories

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Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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A drug dosage regimen describes the specific instructions and schedule for administering a drug to a patient. It considers factors such as drug dosage, frequency, route of administration, and duration of treatment. Designing an appropriate dosage regimen for a patient aims to achieve a target drug concentration at the site of action.
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During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Related Experiment Video

Updated: Jul 17, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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CEHMR: Curriculum learning enhanced hierarchical multi-label classification for medication recommendation.

Mengxuan Sun1, Jinghao Niu2, Xuebing Yang2

  • 1State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.

Artificial Intelligence in Medicine
|September 6, 2023
PubMed
Summary

This study introduces a new framework for medication recommendation (MR) that improves patient care by considering medication hierarchies and training example difficulty. The CEHMR model achieves state-of-the-art results on the MIMIC-III database.

Keywords:
Curriculum learningEHRHierarchical multi-label classificationMedication recommendation

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support

Background:

  • Medication recommendation (MR) is crucial for clinical decision-making, especially for patients with multiple conditions.
  • Current deep learning approaches for MR, while advanced, have limitations in handling label hierarchies and training data complexity.
  • Existing methods often overlook the hierarchical structure of medications and the varying difficulty of training examples.

Purpose of the Study:

  • To propose a novel framework, CEHMR (Curriculum learning Enhanced Hierarchical multi-label classification for MR), to address limitations in current medication recommendation systems.
  • To leverage the hierarchical structure of medication codes for improved label dependency modeling.
  • To enhance the training process by incorporating a curriculum learning strategy to manage diverse example difficulties.

Main Methods:

  • Developed a hierarchical multi-label classifier incorporating a learnable gate fusion layer to capture both local and global hierarchical medication information.
  • Implemented a bootstrap-based curriculum learning strategy to progressively train the model on examples from easy to hard.
  • Utilized the inherent hierarchical structure of standard medication codes as prior knowledge for dependency modeling.

Main Results:

  • The proposed CEHMR framework achieved state-of-the-art performance on the MIMIC-III database, outperforming seven baseline methods.
  • Extensive ablation studies confirmed the effectiveness of individual components within the CEHMR framework.
  • The model demonstrated improved accuracy in predicting effective medication combinations for patients.

Conclusions:

  • The CEHMR framework effectively integrates medication hierarchy and curriculum learning to advance medication recommendation.
  • This approach offers a more robust and accurate method for clinical decision support systems.
  • The findings suggest a promising direction for improving personalized medicine through advanced AI techniques.