PlainHealth (Regulated Accessible Technology for Easier Communication and Interaction in Healthcare) is a project under the AEI 2025 Proof of Concept call, aimed at validating accessible tools in real-world settings to improve communication between healthcare professionals, patients, and caregivers.
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:
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.
Public administrations are rapidly advancing towards the provision of basic and extended services for the citizen through the Web (EU eGovernment Report 2014). In addition to the decrement of costs, this effort will support all peoples civil right to have access to all public services (including people with disabilities and elderly people). For this reason the EU started diverse initiatives to Meeting new societal needs by using emerging technologies in the public sector in order to foster efficient, open citizen-centric public services.
The recent massive growth in online media and the rise of user-authored content (e.g weblogs, Twitter, Facebook) has lead to challenges of how to access and interpret these strongly multilingual data, in a timely, efficient, and affordable manner. Scientifically, streaming online media pose new challenges, due to their shorter, noisier, and more colloquial nature. Moreover, they form a temporal stream strongly grounded in events and context. Consequently, existing language technologies fall short onaccuracy, scalability and portability. The goal of this project is to deliver.
The aim of this project is to define and develop information extraction and retrieval techniques based on texts from the medical domain. This will be carried out following two basic tasks: firstly, processing scientific documents in English about pharmacology, and secondly, processing informative texts about health topics in other languages such as Spanish and Arabic.
The aim is to research in technology based on automatic language processing for information location and retrieval from medical texts and other resources (reports, electronic medical records, scientific documentation, etc..) specially in Spanish language. Thus, we worked in browsers with different levels of complexity that integrate medical domain-specific resources and terminology (UMLS, SNOMED, etc.) and with different treatment of syntactic and semantic levels.
MAVIR Consortium is a research network co-funded by the Regional Government of Madrid under the IV Plan Regional de Investigación Científica e Innovación Tecnológica (IV PRICIT) integrating a multi-disciplinar team made of scientists, engineers, linguists and documentalists working together on two main areas:Human Language Technologies and Scientific Communication via WWW. The thematic network suggested includes 25 doctors organised in 6 research groups (UNED, UAM, UC3M, UEM, UPM and CINDOC) of the CM that from a multi-discipline perspective complement in various dimensions: academic vs.
BRAVO is devoted to research on technologies to improve the answers search in both text and voice, and the main result is a platform for a modular answers search system which allows to measure the improvement of different techniques for questions classification, answer extraction, passages retrieval, etc. SPINDEL is one of the techniques developed in this project, an entity recognizer which, regardless of language, applies machine learning based on bootstrapping.