Persona:
Araujo Serna, M. Lourdes

Foto de perfil
Dirección de correo electrónico
ORCID
0000-0002-7657-4794
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Araujo Serna
Nombre de pila
M. Lourdes
Nombre

Filtros

2009 - 200922010 - 201982020 - 202412
Restablecer filtros

Ajustes

Autor: Araujo Serna, M. Lourdes × search.filters.applied.f.itemFacultad: E.T.S. de Ingeniería Informática ×

Resultados de la búsqueda

Mostrando 1 - 10 de 22
  • Miniatura
    Publicación
    Detecting Signs of Non-suicidal Self-Injury in Psychiatric Medical Reports Using Language Analysis
    (Sociedad Española para el Procesamiento del Lenguaje Natural, 2022) Reneses, Blanca; Sevilla-Llewellyn-Jones, Julia; Martínez-Capella, Ignacio; Seara-Aguilar, Germán; Martínez Romo, Juan; Araujo Serna, M. Lourdes
    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.
  • Miniatura
    Publicación
    RoBERTime: un nuevo modelo para la detección de expresiones temporales en español
    (Sociedad Española para el Procesamiento del Lenguaje Natural, 2023-03) Sánchez de Castro Fernández, Alejandro; Araujo Serna, M. Lourdes; Martínez Romo, Juan
    Temporal expressions are all those words that refer to temporality. Their detection or extraction is a complex task, since it depends on the domain of the text, the language and the way they are written. Their study in Spanish and more specifically in the clinical domain is scarce, mainly due to the lack of annotated corpora. In this paper we propose the use of large language models to address the task, comparing the performance of five models of different characteristics. After a process of experimentation and fine tuning, a new model called RoBERTime is created for the detection of temporal expressions in Spanish, especially focused in the clinical domain. This model is publicly available. RoBERTime achieves state-of-the-art results in the E3C and Timebank corpora, being the first public model for the detection of temporal expressions in Spanish specialized in the clinical domain.
  • Miniatura
    Publicación
    Building a framework for fake news detection in the health domain
    (San Francisco CA: Public Library of Science, 2024-07-08) Martinez Rico, Juan R.; Araujo Serna, M. Lourdes; Martínez Romo, Juan; Bongelli, Ramona
    Disinformation in the medical field is a growing problem that carries a significant risk. Therefore, it is crucial to detect and combat it effectively. In this article, we provide three elements to aid in this fight: 1) a new framework that collects health-related articles from verification entities and facilitates their check-worthiness and fact-checking annotation at the sentence level; 2) a corpus generated using this framework, composed of 10335 sentences annotated in these two concepts and grouped into 327 articles, which we call KEANE (faKe nEws At seNtence lEvel); and 3) a new model for verifying fake news that combines specific identifiers of the medical domain with triplets subject-predicate-object, using Transformers and feedforward neural networks at the sentence level. This model predicts the fact-checking of sentences and evaluates the veracity of the entire article. After training this model on our corpus, we achieved remarkable results in the binary Classification of sentences (check-worthiness F1: 0.749, fact-checking F1: 0.698) and in the final classification of complete articles (F1: 0.703). We also tested its performance against another public dataset and found that it performed better than most systems evaluated on that dataset. Moreover, the corpus we provide differs from other existing corpora in its duality of sentence-article annotation, which can provide an additional level of justification of the prediction of truth or untruth made by the model.
  • Miniatura
    Publicación
    Understanding and Improving Disability Identification in Medical Documents
    (IEEE, 2020) Fabregat Marcos, Hermenegildo; Martínez Romo, Juan; Araujo Serna, M. Lourdes
    Disabilities are a problem that affects a large number of people in the world. Gathering information about them is crucial to improve the daily life of the people who suffer from them but, since disabilities are often strongly associated with different types of diseases, the available data are widely dispersed. In this work we review existing proposal for the problem, making an in-depth analysis, and from it we make a proposal that improves the results of previous systems. The analysis focuses on the results of the participants in DIANN shared task was proposed (IberEval 2018), devoted to the detection of named disabilities in electronic documents. In order to evaluate the proposed systems using a common evaluation framework, a corpus of documents, in both English and Spanish, was gathered and annotated. Several teams participated in the task, either using classic methods or proposing specific approaches to deal effectively with the complexities of the task. Our aim is to provide insight for future advances in the field by analyzing the participating systems and identifying the most effective approaches and elements to tackle the problem. We have validated the lessons learned from this analysis through a new proposal that includes the most promising elements used by the participating teams. The proposed system improves, for both languages, the results obtained during the task.
  • Miniatura
    Publicación
    Anonimización de Informes Médicos
    (Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial, 2021-09-15) Gaitán Rivas, José Antonio; Araujo Serna, M. Lourdes; Martínez, Raquel
    Con el objetivo de mejorar la salud y seguridad de los pacientes cada vez existe un mayor interés en gestionar eficientemente el contenido de los historiales clínicos electrónicos. Dichos informes médicos están escritos principalmente en lenguaje natural, por lo que contienen información no estructurada generalizadamente, haciéndose imprescindibles tecnologías de Minería de Textos y de PLN (Procesamiento de Lenguaje Natural) para su explotación. Con técnicas apropiadas de dichas tecnologías se ayuda en la toma de decisiones clínicas o se facilita la reutilización de medicamentos, entre muchas otras ventajas. Sin embargo, los registros clínicos con información de salud protegida (PHI o Protected Health Information) no pueden ser compartidos directamente debido a restricciones relacionadas con la protección de datos sobre dicha información privada de los pacientes. Es necesaria pues, una anonimización o disociación de dichos registros antes de poder ser usados externamente, debiéndose eliminar total o parcialmente toda información que permita identificar al paciente. La base del presente trabajo ha sido la tarea de evaluación MEDDOCAN (Medical Document Anonymization), a la que puede accederse en https://temu.bsc.es/meddocan , que forma parte de la iniciativa IberLEF 2019, y con la que se organizó un desafío para la comunidad hispano-hablante, con el objetivo de diseñar sistemas eficientes de anonimización de documentos médicos escritos en español. La tarea de MEDDOCAN se estructura en dos subtareas:  Identificación y clasificación de entidades (nombres de paciente, teléfonos, etc.)  Detección de texto sensible La evaluación oficial de la tarea, por tanto, engloba los resultados de ambas subtareas. El corpus está formado por 1.000 estudios de casos clínicos, y cada uno de ellos cuenta, de forma anexa, con expresiones PHI realizadas por profesionales. 4 Del total de 1.000 casos, se reservó el 50% (500 casos) para entrenamiento de la tarea, un 25% (250 casos) para labores de desarrollo, y el otro 25% (250 casos) para pruebas. En el desafío participaron 18 equipos, de un total de 8 nacionalidades distintas, y el mejor resultado, basado en la métrica F-score, fue de 0.9360 para la subtarea 1 (“Identificación y clasificación de entidades”) y de 0.9611 para la subtarea 2 (“Detección de texto sensible”). A lo largo del presente trabajo estudiaremos y compararemos los datos proporcionados por los organizadores de la tarea, y propondremos un sistema que implementa una solución simple mediante técnicas de Aprendizaje Automático y Minería de Textos. Finalmente analizaremos los resultados obtenidos con dicho sistema y serán comparados con los de los participantes en la tarea, exponiendo las ventajas e inconvenientes para la arquitectura escogida, respecto a las presentadas. En dichas conclusiones incorporaremos un listado de posibles mejoras o implementaciones futuras recomendadas para mejorar el rendimiento.
  • Miniatura
    Publicación
    Web spam detection : new classification features based on qualified link analysis and language models
    (Institute of Electrical and Electronics Engineers (IEEE), 2010-09-01) Araujo Serna, M. Lourdes; Martínez Romo, Juan
    Web spam is a serious problem for search engines because the quality of their results can be severely degraded by the presence of this kind of page. In this paper, we present an efficient spam detection system based on a classifier that combines new link-based features with language-model (LM)-based ones. These features are not only related to quantitative data extracted from the Web pages, but also to qualitative properties, mainly of the page links.We consider, for instance, the ability of a search engine to find, using information provided by the page for a given link, the page that the link actually points at. This can be regarded as indicative of the link reliability. We also check the coherence between a page and another one pointed at by any of its links. Two pages linked by a hyperlink should be semantically related, by at least a weak contextual relation. Thus, we apply an LM approach to different sources of information from aWeb page that belongs to the context of a link, in order to provide high-quality indicators of Web spam. We have specifically applied the Kullback–Leibler divergence on different combinations of these sources of information in order to characterize the relationship between two linked pages. The result is a system that significantly improves the detection of Web spam using fewer features, on two large and public datasets such as WEBSPAM-UK2006 and WEBSPAM-UK2007.
  • Miniatura
    Publicación
    Automatic Recommendation of Forum Threads and Reinforcement Activities in a Data Structure and Programming Course
    (MDPI, 2023-09-21) Plaza Morales, Laura; Araujo Serna, M. Lourdes; López Ostenero, Fernando; Martínez Romo, Juan
    Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreover, online learning enhances accessibility to education, breaking down geographical and economical boundaries. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports e-learning students by tailoring materials and reinforcement activities to students’ needs. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content. The primary contributions of this research are three. Firstly, it centers on a remarkably specialized and novel domain; secondly, it introduces an effective recommendation approach exclusively guided by the student’s query. Thirdly, the recommendations not only provide answers to immediate questions, but also encourage further learning through the recommendation of supplementary activities.
  • Miniatura
    Publicación
    Discovering HIV related information by means of association rules and machine learning
    (Nature Research, 2022-10-22) Araujo Serna, M. Lourdes; Martínez Romo, Juan; Bisbal, Otilia; Sanchez de Madariaga, Ricardo; The Cohort of the National AIDS Network (CoRIS); https://orcid.org/0000-0003-3746-3378
    Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts.
  • Miniatura
    Publicación
    Detecting malicious tweets in trending topics using a statistical analysis of language
    (Elsevier, 2013-06-01) Martínez Romo, Juan; Araujo Serna, M. Lourdes
    Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody’s lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe- art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.
  • Miniatura
    Publicación
    Identifying patterns for unsupervised grammar induction
    (2010-07-15) Santamaría Martínez de la Casa, Jesús; Araujo Serna, M. Lourdes
    This paper describes a new method for unsupervised grammar induction based on the automatic extraction of certain patterns in the texts. Our starting hypothesis is that there exist some classes of words that function as separators, marking the beginning or the end of new constituents. Among these separators we distinguish those which trigger new levels in the parse tree. If we are able to detect these separators we can follow a very simple procedure to identify the constituents of a sentence by taking the classes of words between separators. This paper is devoted to describe the process that we have followed to automatically identify the set of separators from a corpus only annotated with Part-of-Speech (POS) tags. The proposed approach has allowed us to improve the results of previous proposals when parsing sentences fromtheWall Street Journal corpus.