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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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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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Sistema de modelo de lenguaje grande aumentado por recuperación para contraindicaciones farmacológicas integrales

Byeonghun Bang1, Jongsuk Yoon1, Dong-Jin Chang2

  • 1Department of Computer Engineering, Hongik University, Seoul, 04066 South Korea.

Health information science and systems
|January 14, 2026
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Resumen

Este estudio mejora los modelos de lenguaje grandes (LLM) para contraindicaciones farmacéuticas utilizando un pipeline de Generación Aumentada por Recuperación (RAG). El enfoque RAG mejoró significativamente la precisión en la identificación de interacciones farmacológicas, garantizando una orientación farmacológica más segura.

Palabras clave:
Contraindicación farmacológicaModelos de lenguaje grandesGeneración aumentada por recuperación

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Área de la Ciencia:

  • Informática Médica
  • Inteligencia Artificial en Atención Médica
  • Farmacovigilancia

Sus antecedentes:

  • Los modelos de lenguaje grandes (LLM) muestran versatilidad pero enfrentan desafíos en la atención médica, especialmente para datos críticos como las contraindicaciones farmacéuticas.
  • La información precisa y confiable sobre las contraindicaciones de los medicamentos es esencial para la seguridad del paciente y la atención médica efectiva.
  • Las aplicaciones existentes de LLM requieren mejoras para manejar de manera confiable información médica compleja, como interacciones medicamentosas y advertencias específicas del paciente.

Objetivo del estudio:

  • Mejorar la capacidad de los LLM para identificar con precisión las contraindicaciones farmacéuticas.
  • Implementar y evaluar un pipeline de Generación Aumentada por Recuperación (RAG) para mejorar la detección de contraindicaciones.
  • Reducir la incertidumbre en las decisiones de prescripción y toma de medicamentos a través de información precisa sobre contraindicaciones.

Principales métodos:

  • Se utilizó GPT-4o-mini de OpenAI como LLM base y text-embedding-3-small para las incrustaciones.
  • Se integró un sistema de recuperación híbrido con re-clasificación utilizando el framework LangChain.
  • Se utilizaron datos de Revisión de Utilización de Medicamentos (DUR) centrados en contraindicaciones por edad, embarazo y uso concomitante de medicamentos.

Principales resultados:

  • La precisión de los LLM de referencia para las contraindicaciones osciló entre 0,49 y 0,57.
  • El pipeline RAG mejoró significativamente la precisión a 0,94 (edad), 0,87 (embarazo) y 0,89 (uso concomitante).
  • Demostró una reducción sustancial de la incertidumbre en las decisiones de prescripción y toma de medicamentos.

Conclusiones:

  • Aumentar los LLM con un framework RAG mejora sustancialmente la precisión de la información sobre contraindicaciones farmacéuticas.
  • El pipeline RAG desarrollado ofrece una solución prometedora para la recuperación confiable de información sobre seguridad de medicamentos.
  • Este enfoque puede mejorar la toma de decisiones clínicas y la seguridad del paciente en la gestión de medicamentos.