Citation

Martinez-Romo J, Reneses B, Martínez-Capella I, Araujo L, Sevilla-Llewellyn-Jones J, Seara-Aguilar G. Procesamiento del Lenguaje Natural 2022; 69: 129-140.

Copyright

(Copyright © 2022)

DOI

10.26342/2022-69-11

PMID

unavailable

Abstract

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. © 2022 Sociedad Española para el Procesamiento del Lenguaje Natural.

Language: es

Keywords

machine learning; neural networks; language analysis; Non-suicidal Self-injury detection