Document-level adverse drug reaction event extraction on electronic health records in Spanish

• Autores: Sara Santiso, Arantza Casillas Rubio , Alicia I. Pérez de Pereyra, Maite Oronoz Anchordoqui , Koldobika Gojenola Galletebeitia
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 56, 2016, págs. 49-56
• Idioma: inglés
• Títulos paralelos:
  • Extracción a nivel de documento de reacciones adversas a medicamentos en informes médicos electrónicos en español
• Enlaces
  • Texto completo
• Resumen
  • español

    Presentamos un sistema de extracción de Reacciones Adversas a Medicamentos (RAMs) para Informes Médicos Electrónicos escritos en español. El objetivo del sistema es asistir a expertos en farmacia cuando tienen que decidir si un paciente padece o no una o más RAMs. El núcleo del sistema es un modelo predictivo inferido de un corpus etiquetado manualmente, que cuenta con características semánticas y sintácticas. Este modelo es capaz de extraer RAMs de parejas enfermedad-medicamento en un informe dado. Finalmente, las RAMs extraídas automáticamente son post-procesadas usando un heurístico para presentar la información de una forma compacta. Esta fase ofrece los medicamentos y enfermedades del documento con su frecuencia, y también une las parejas relacionadas como RAMs. En resumen, el sistema no sólo presenta las RAMs en el texto sino que también da información concisa a petición de los expertos en farmacia (los usuarios potenciales del sistema).

  • English

    We outline an Adverse Drug Reaction (ADRs) extraction system for Electronic Health Records (EHRs) written in Spanish. The goal of the system is to assist experts on pharmacy in making the decision of whether a patient suffers from one or more ADRs. The core of the system is a predictive model inferred from a manually tagged corpus that counts on both semantic and syntactically features. This model is able to extract ADRs from disease-drug pairs in a given EHR. Finally, the ADRs automatically extracted are post-processed using a heuristic to present the information in a compact way. This stage reports the drugs and diseases of the document together with their frequency, and it also links the pairs related as ADRs. In brief, the system not only presents the ADRs in the text but also provides concise information on request by experts in pharmacy (the potential users of the system).

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