Persona:
Martínez Romo, Juan

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0000-0002-6905-7051
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Martínez Romo
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Juan
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2020 - 20248
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Fecha de finalización: 2024 × Fecha de inicio: 2020 ×

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Mostrando 1 - 8 de 8
  • Miniatura
    Publicación
    Semi‑supervised incremental learning with few examples for discovering medical association rules
    (BioMed Central, 2022) Sánchez‑de‑Madariaga, Ricardo; Cantero Escribano, José Miguel; Martínez Romo, Juan; Araujo Serna, M. Lourdes
    Background: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time‑consuming. The purpose of this research is to design a new semi‑supervised algorithm that performs like supervised algorithms but uses an affordable amount of training data. Methods: In this work we propose a new semi‑supervised data mining model that combines unsupervised techniques (Fisher’s exact test) with limited supervision. Starting with a small seed of annotated data, the model improves results (F‑measure) obtained, using a fully supervised system (standard supervised ML algorithms). The idea is based on utilising the agreement between the predictions of the supervised system and those of the unsupervised techniques in a series of iterative steps. Results: The new semi‑supervised ML algorithm improves the results of supervised algorithms computed using the F‑measure in the task of mining medical association rules, but training with an affordable amount of manually annotated data. Conclusions: Using a small amount of annotated data (which is easily achievable) leads to results similar to those of a supervised system. The proposal may be an important step for the practical development of techniques for mining association rules and generating new valuable scientific medical knowledge.
  • Miniatura
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    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, Juan
    Background 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.
  • Miniatura
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    Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty
    (Institute of Electrical and Electronics Engineers, 2020-12-02) Araujo Serna, M. Lourdes; López Ostenero, Fernando; Martínez Romo, Juan; Plaza Morales, Laura
    Learning analytics has emerged as a promising tool for optimizing the learning experience and results, especially in online educational environments. An important challenge in this area is identifying the most difficult topics for students in a subject, which is of great use to improve the quality of teaching by devoting more effort to those topics of greater difficulty, assigning them more time, resources and materials. We have approached the problem by means of natural language processing techniques. In particular, we propose a solution based on a deep learning model that automatically extracts the main topics that are covered in educational documents. This model is next applied to the problem of identifying the most difficult topics for students in a subject related to the study of algorithms and data structures in a Computer Science degree. Our results show that our topic identification model presents very high accuracy (around 90 percent) and may be efficiently used in learning analytics applications, such as the identification and understanding of what makes the learning of a subject difficult. An exhaustive analysis of the case study has also revealed that there are indeed topics that are consistently more difficult for most students, and also that the perception of difficulty in students and teachers does not always coincide with the actual difficulty indicated by the data, preventing to pay adequate attention to the most challenging topics.
  • 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
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    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
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    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. Lourdes
    Background 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.
  • Miniatura
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    Generation of social network user profiles and their relationship with suicidal behaviour
    (Sociedad Española para el Procesamiento del Lenguaje Natural, 2024) Fernández Hernández, Jorge; Araujo Serna, M. Lourdes; Martínez Romo, Juan
    Actualmente el suicidio es una de las principales causas de muerte en el mundo, por lo que poder caracterizar a personas con esta tendencia puede ayudar a prevenir posibles intentos de suicidio. En este trabajo se ha recopilado un corpus, llamado SuicidAttempt en español compuesto por usuarios con o sin menciones explícitas de intentos de suicidio, usando la aplicación de mensajería Telegram. Para cada uno de los usuarios se han anotado distintos rasgos demográficos de manera semi-automática mediante el empleo de distintos sistemas, en unos casos supervisados y en otros no supervisados. Por último se han analizado estos rasgos recogidos, junto con otros lingüísticos extraídos de los mensajes de los usuarios, para intentar caracterizar distintos grupos en base a su relación con el comportamiento suicida. Los resultados sugieren que la detección de estos rasgos demográficos y psicolingüísticos permiten caracterizar determinados grupos de riesgo y conocer en profundidad los perfiles que realizan dichos actos.
  • Miniatura
    Publicación
    Detección de Indicios de Autolesiones No Suicidas en Informes Médicos de Psiquiatría Mediante el Análisis del Lenguaje
    (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.