Detección de Indicios de Autolesiones No Suicidas en Informes Médicos de Psiquiatría Mediante el Análisis del Lenguaje

• Autores: Juan Martínez Romo , Lourdes Araujo , Blanca Reneses, Julia Sevilla Llewellyn Jones, Ignacio Martínez Capella, Germán Seara Aguilar
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 69, 2022, págs. 129-140
• Idioma: español
• Títulos paralelos:
  • Detecting Signs of Non-suicidal Self-Injury in Psychiatric Medical Reports Using Language Analysis
• Enlaces
  • Texto completo
• Resumen
  • español

    La autolesión no suicida, a menudo denominada autolesión, es el acto de dañarse deliberadamente el propio cuerpo, como cortarse o quemarse. Normalmente, no pretende ser un intento de suicidio. En este trabajo se presenta un sistema de detección de indicios de autolesiones no suicidas, basado en el análisis del lenguaje, sobre un conjunto anotado de informes médicos obtenidos del servicio de psiquiatría de un Hospital público madrileño. Tanto la explicabilidad como la precisión a la hora de predecir los casos positivos, son los dos principales objetivos de este trabajo. Para lograr este fin se han desarrollado dos sistemas supervisados de diferente naturaleza. Por un lado se ha llevado a cabo un proceso de extracción de diferentes rasgos centrados en el propio mundo de las autolesiones mediante técnicas de procesamiento del lenguaje natural para alimentar posteriormente un clasificador tradicional. Por otro lado, se ha implementado un sistema de aprendizaje profundo basado en varias capas de redes neuronales convolucionales, debido a su gran desempeño en tareas de clasificación de textos. El resultado es el funcionamiento de dos sistemas supervisados con un gran rendimiento, en donde destacamos el sistema basado en un clasificador tradicional debido a su mejor predicción de clases positivas y la mayor facilidad de cara a explicar sus resultados a los profesionales sanitarios.

  • English

    Non-suicidal self-injury, often referred to as self-injury, is the act of deliberately harming one’s own body, such as cutting or burning oneself. It is not usually intended as a suicide attempt. This paper presents a system for detecting signs of non-suicidal self-injury, based on language analysis, on an annotated set of medical reports obtained from the psychiatric service of a public hospital in Madrid. Both explainability and accuracy in predicting positive cases are the two main objectives of this work. In order to achieve this goal, two supervised systems of different natures have been developed. On the one hand, a process of extraction of different features focused on the world of self-injury itself has been carried out using natural language processing techniques to subsequently feed a traditional classifier. On the other hand, a deep learning system based on several layers of convolutional neural networks, due to its high performance in text classification tasks. The result are two supervised systems with high performance, where we highlight the system based on a traditional classifier due to its better prediction of positive classes and the greater ease to explain its results to health professionals.

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