Persona: Araujo Serna, M. Lourdes
Cargando...
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
4 resultados
Resultados de la búsqueda
Mostrando 1 - 4 de 4
Publicación A keyphrase-based approach for interpretable ICD-10 code classification of Spanish medical reports(Elsevier, 2021) Fabregat Marcos, Hermenegildo; Duque Fernández, Andrés; Araujo Serna, M. Lourdes; Martínez Romo, JuanBackground and objectives: The 10th version of International Classification of Diseases (ICD-10) codification system has been widely adopted by the health systems of many countries, including Spain. However, manual code assignment of Electronic Health Records (EHR) is a complex and time-consuming task that requires a great amount of specialised human resources. Therefore, several machine learning approaches are being proposed to assist in the assignment task. In this work we present an alternative system for automatically recommending ICD-10 codes to be assigned to EHRs. Methods: Our proposal is based on characterising ICD-10 codes by a set of keyphrases that represent them. These keyphrases do not only include those that have literally appeared in some EHR with the considered ICD-10 codes assigned, but also others that have been obtained by a statistical process able to capture expressions that have led the annotators to assign the code. Results: The result is an information model that allows to efficiently recommend codes to a new EHR based on their textual content. We explore an approach that proves to be competitive with other state-of-the-art approaches and can be combined with them to optimise results. Conclusions: In addition to its effectiveness, the recommendations of this method are easily interpretable since the phrases in an EHR leading to recommend an ICD-10 code are known. Moreover, the keyphrases associated with each ICD-10 code can be a valuable additional source of information for other approaches, such as machine learning techniques.Publicación Deep learning approach for negation trigger and scope recognition(Sociedad Española para el Procesamiento del Lenguaje Natural, 2019) Fabregat Marcos, Hermenegildo; Araujo Serna, M. Lourdes; Martínez Romo, JuanLa detección automática de los distintos elementos de la negación es un frecuente tema de estudio debido a su alto impacto en diversas tareas de procesamiento de lenguaje natural. Este articulo presenta un sistema basado en deep learning y de arquitectura no dependiente del idioma para la detección automática tanto de disparadores como del alcance de la negación para inglés y español. El sistema presentado obtiene para ingles resultados comparables a los obtenidos en recientes trabajos por sistemas más complejos. Para español destacan los resultados obtenidos en la detección de claves de negación. Por último, los resultados para el reconocimiento del alcance de la negación, son similares a los obtenidos en inglés.Publicación Understanding and Improving Disability Identification in Medical Documents(IEEE, 2020) Fabregat Marcos, Hermenegildo; Martínez Romo, Juan; Araujo Serna, M. LourdesDisabilities 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.Publicación Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction(Elsevier, 2023-02) Fabregat Marcos, Hermenegildo; Duque Fernández, Andrés; Martínez Romo, Juan; Araujo Serna, M. LourdesBackground and Objectives: Named Entity Recognition (NER) and Relation Extraction (RE) are two of the most studied tasks in biomedical Natural Language Processing (NLP). The detection of specific terms and entities and the relationships between them are key aspects for the development of more complex automatic systems in the biomedical field. In this work, we explore transfer learning techniques for incorporating information about negation into systems performing NER and RE. The main purpose of this research is to analyse to what extent the successful detection of negated entities in separate tasks helps in the detection of biomedical entities and their relationships. Methods: Three neural architectures are proposed in this work, all of them mainly based on Bidirectional Long Short-Term Memory (Bi-LSTM) networks and Conditional Random Fields (CRFs). While the first architecture is devoted to detecting triggers and scopes of negated entities in any domain, two specific models are developed for performing isolated NER tasks and joint NER and RE tasks in the biomedical domain. Then, weights related to negation detection learned by the first architecture are incorporated into those last models. Two different languages, Spanish and English, are taken into account in the experiments. Results: Performance of the biomedical models is analysed both when the weights of the neural networks are randomly initialized, and when weights from the negation detection model are incorporated into them. Improvements of around 3.5% of F-Measure in the English language and more than 7% in the Spanish language are achieved in the NER task, while the NER+RE task increases F-Measure scores by more than 13% for the NER submodel and around 2% for the RE submodel. Conclusions: The obtained results allow us to conclude that negation-based transfer learning techniques are appropriate for performing biomedical NER and RE tasks. These results highlight the importance of detecting negation for improving the identification of biomedical entities and their relationships. The explored echniques show robustness by maintaining consistent results and improvements across different tasks and languages.