Isabel Segura Bedmar (Research lines)
Esta línea de investigación se centra en el desarrollo y aplicación de técnicas avanzadas de PLN y algoritmos de aprendizaje profundo para la extracción, análisis y aprovechamiento de información clínica contenida en historias clínicas electrónicas (HCE) no estructuradas.
Busca transformar grandes volúmenes de datos textuales en información estructurada y procesable, con el objetivo de mejorar la práctica clínica, apoyar la toma de decisiones médicas personalizadas, y facilitar la investigación, especialmente en estudios epidemiológicos que requieren la revisión y análisis de un extenso número de registros clínicos.
Este enfoque no solo promete una optimización significativa de los recursos y tiempo en la investigación médica sino también una adaptabilidad a diversos contextos clínicos y patologías, subrayando la versatilidad y potencial transformador de las tecnologías de PLN y deep learning en el ámbito de la salud.
Proyectos más destacados:
- Extracción de información clínica usando deep learning y técnicas de Big Data (DeepEMR)
Our research lines reflect the journey from our beginnings, showcasing how the foundations laid in areas such as natural language processing, biomedicine, web development, and accessibility have evolved into the current cutting-edge directions.
This journey demonstrates not only our adaptability and growth but also underpins our present investigations. These research lines, which continue to be relevant, are as follows:
Extraction and Retrieval of Information in the Biomedical Domain
Research and development of automatic tools to manage the exponential growth of data and biomedical publications, facilitating the extraction of valuable knowledge for professionals in the field.
Methodological Frameworks for the Development of Accessible Web Applications
Creation and guidelines for the application of accessible applications and websites through the methodological support of AWA, aimed at integrating accessibility as an essential requirement in web development.
Accessibility in the Educational Environment
Research and development of Information and Communication Technologies (ICT) that can overcome accessibility barriers in e-learning and educational content management systems, promoting inclusive education for all.
Question Answering Systems
Research and development of Question Answering Systems (QAS) that allow obtaining precise information through natural language questions, addressing challenges.
Recognition of Named Entities and Temporal Expressions
Research in the recognition and classification of named entities and the automatic processing of temporal information in texts is needed to improve the performance of Natural Language Processing applications.
Conceptual Modeling, Integrity Constraints, and Business Rules
Research on data models to improve the management, adaptability, and integration of information systems, ensuring the accuracy and completeness of databases.