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Codificación de la Clasificación Internacional Automatizada de Enfermedades basada en la Inteligencia Artificial: una
Seyyedeh Fatemeh Mousavi Baigi1,2, Masoumeh Sarbaz1, Ali Darroudi1,2
1Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
Journal of medical signals and sensors
|August 27, 2025
Resumen
La inteligencia artificial (IA) mejora la codificación clínica automatizada para la Clasificación Internacional de Enfermedades (CIE). A pesar de los desafíos como el desequilibrio de los datos, la IA ofrece beneficios como una mayor precisión y eficiencia en la codificación de la atención médica.
Área de la Ciencia:
- Informática de la salud
- Inteligencia artificial en el cuidado de la salud
- Sistemas de codificación clínica
Sus antecedentes:
- La codificación clínica automatizada utilizando IA es crucial para la eficiencia de la atención médica.
- La precisión de la codificación de la Clasificación Internacional de Enfermedades (CIE) es vital para la facturación y la investigación.
- Los procesos de codificación manual existentes se enfrentan a desafíos de velocidad y consistencia.
Objetivo del estudio:
- Revisar sistemáticamente los conocimientos actuales sobre la codificación automatizada del ICD basada en la IA.
- Identificar y sintetizar los retos, los beneficios y las direcciones futuras de la investigación en este campo.
- Proporcionar ideas para el avance de los sistemas de codificación automatizados de la CIE.
Principales métodos:
- Búsqueda sistemática de literatura realizada el 1 de enero de 2024 en las principales bases de datos (PubMed, Embase, Scopus, Web of Science).
- Cumplimiento de las directrices sobre las partidas de información preferidas para las revisiones sistemáticas y los metanálisis (PRISMA).
- Inclusión de estudios centrados en los desafíos, ventajas y brechas de investigación de la codificación ICD impulsada por la IA; análisis de 8 estudios seleccionados de 12.641 registros.
Principales resultados:
- Se identificaron seis desafíos clave: espacio extenso en las etiquetas, datos desequilibrados, documentos extensos, interpretabilidad, preocupaciones éticas y falta de transparencia.
- Se destacaron diez beneficios principales: mejora de la toma de decisiones, estandarización de los datos y mayor precisión de la codificación.
- Se proponen ocho direcciones de investigación futuras: colaboración interdisciplinaria, transferencia de aprendizaje, transparencia y aprendizaje activo.
Conclusiones:
- La codificación automática de ICD basada en IA presenta un potencial significativo para revolucionar la codificación clínica.
- Abordar los desafíos identificados es crucial para una implementación exitosa.
- Se recomienda una mayor investigación en áreas como el aprendizaje de transferencia y la transparencia para optimizar los sistemas de codificación impulsados por IA.
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