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Pruebas basadas en el contexto: estudio piloto para extraer, reutilizar y distribuir datos publicados sobre OUD

Alexander Sheppard1, Carly Milliren2, Douglas Schatz3

  • 1University of California, Los Angeles, Los Angeles, California. ORCID: https://orcid.org/0009-0002-6259-6738.

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Este estudio introduce un nuevo método de organización de datos para sintetizar una amplia investigación sobre el trastorno por consumo de opiáceos. Este enfoque hace que la información compleja de OUD sea accesible para mejorar la atención al paciente y la investigación.

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Área de la Ciencia:

  • * Informática médica
  • * Ciencia de los datos
  • * Salud pública

Sus antecedentes:

  • * El trastorno por consumo de opiáceos (OUD, por sus siglas en inglés) representa un importante desafío para la salud mundial con una extensa literatura de investigación.
  • * La síntesis del vasto cuerpo de investigación de OUD es difícil para la mayoría de las personas.
  • * Los métodos de recuperación de información existentes pueden no facilitar la comprensión rápida de los datos de OUD.

Objetivo del estudio:

  • * Pilotear un método de extracción e integración de datos para la investigación OUD publicada.
  • * Reutilizar los datos OUD existentes para una mejor comprensión y distribución global.
  • * Desarrollar una base de conocimientos fácilmente accesible sobre el trastorno por consumo de opiáceos.

Principales métodos:

  • * Se utilizó una estructura de árbol de datos para organizar la información de 752 artículos seleccionados de PubMed sobre OUD.
  • * Se examinaron los artículos, excluidos los artículos de revisión, los informes de casos y las pequeñas series de muestras.
  • * Se utilizó un sistema de notas jerárquicas con relaciones padre-hijo para la integración de datos.

Principales resultados:

  • * Cuatro tipos de notas (tema, población, descripción del valor numérico, valor numérico) resultaron suficientes para la descripción de los datos.
  • * La integración de datos mediante notas compartidas redujo el número total de notas en un 72,9%.
  • * La estructura de árbol de datos permitió la expansión y reducción flexibles de los nodos de información.

Conclusiones:

  • * El método desarrollado facilita la agrupación fácilmente comprensible de grandes conjuntos de datos.
  • * Esta estrategia permite una extracción eficiente y la accesibilidad de todos los datos publicados OUD.
  • * Se espera que la mejora de la accesibilidad de los datos mejore los resultados de los pacientes y los objetivos de investigación en OUD.