IXHEALTH: An advanced speech recognition system to interact with healthcare information systems

El objetivo del proyecto IXHEALTH es desarrollar una plataforma multilingüe basada en reconocimiento del habla que permita a profesionales de la salud llevar a cabo tareas tales como la redacción de informes médicos, así como interactuar con sistemas de información sanitarios mediante comandos de voz. Todo ello, bajo un mecanismo de seguridad basado en biometría de voz que evite que personas no autorizadas editen información sensible gestionada por este tipo de sistemas. Este proyecto ha sido desarrollado por la empresa VOCALI en conjunto con el grupo de investigación TECNOMOD de la Universidad de Murcia, y financiado por el Instituto de Fomento de la Región de Murcia.The IXHEALTH project aims to develop a multilingual platform based on speech recognition that allows healthcare professionals to perform transcription and dictation activities for the generation of medical reports, as well as to interact with healthcare information systems by means of voice commands. These tasks are performed through a biometric voice-based security mechanism that avoids non-allowed users to edit sensitive data managed by this kind of systems. This project has been developed by the VOCALI enterprise in conjunction with the TECNOMOD research group from the University of Murcia, and it has been founded by the Institute of Promotion from the Region of Murcia.Este trabajo ha sido financiado por el Instituto de fomento de la Región de Murcia (Ref. 2015.08.ID+I.0011

Transcription, indexing and automatic analysis of judicial declarations from phonetic representations and techniques of forensic linguistics

Recientes avances tecnológicos han permitido mejorar los procesos judiciales para la búsqueda de información en los expedientes judiciales asociados a un caso. Sin embargo, cuando técnicos y peritos deben revisar pruebas almacenadas en vídeos y fragmentos de audio, se ven obligados a realizar una búsqueda manual en el documento multimedia para localizar la parte que desean revisar, lo cual es una tarea tediosa y que consume bastante tiempo. Para poder facilitar el desempeño de los técnicos, el presente proyecto consiste en un sistema que permite la transcripción e indexación automática de contenido multimedia basado en tecnologías de deep-learning en entornos de ruido y con múltiples interlocutores, así como la posibilidad de realizar análisis de lingüística forense sobre los datos para ayudar a los peritos a analizar los testimonios de modo que se aporten evidencias sobre la veracidad del mismo.Recent technological advances have made it possible to improve the search for information in the judicial files of the Ministry of Justice associated with a trial. However, when judicial experts examine evidence in multimedia files, such as videos or audio fragments, they must manually search the document to locate the fragment at issue, which is a tedious and time-consuming task. In order to ease this task, we propose a system that allows automatic transcription and indexing of multimedia content based on deep-learning technologies in noise environments and with multiple speakers, as well as the possibility of applying forensic linguistics techniques to enable the analysis of witness statements so that evidence on its veracity is provided.Este proyecto ha sido financiado por el Instituto de Fomento de la Región de Murcia con fondos FEDER dentro del proyecto con referencia 2018.08.ID+I.0025

Trichoderma harzianum T-78 supplementation of compost stimulates the antioxidant defence system in melon plants

[Background] Compost is emerging as an alternative plant growing medium in efforts to achieve more sustainable agriculture. The addition of specific microorganisms such as Trichoderma harzianum to plant growth substrates increases yields and reduces plant diseases, but the mechanisms of such biostimulants and the biocontrol effects are not yet fully understood. In this work we investigated how the addition of citrus and vineyard composts, either alone or in combination with T. harzianum T-78, affects the antioxidant defence system in melon plants under nursery conditions.[Results] Compost application and/or Trichoderma inoculation modulated the antioxidant defence system in melon plants. The combination of citrus compost and Trichoderma showed a biostimulant effect that correlated with an increase in ascorbate recycling enzymes (monodehydroascorbate reductase, dehydroascorbate reductase) and peroxidase. Moreover, the inoculation of both composts with Trichoderma increased the activity of antioxidant enzymes, especially those involved in ascorbate recycling.[Conclusion] Based on the long-established relationship between ascorbic acid and plant defence responses as well as plant growth and development, it can be suggested that ascorbate recycling activities play a major role in the protection provided by Trichoderma and its biostimulant effect and that these outcomes are linked to increases in antioxidant enzymes. We can conclude that the combination of citrus compost and T. harzianum T-78 constitutes a viable, environmentally friendly strategy for improving melon plant production. © 2014 Society of Chemical IndustryThis work was supported by the FPU Programme of the Spanish Ministry of Education and the CYCIT AGL2010-21073 project from the Spanish Ministry of Economy and Competitivity. The study was also carried out as part of the Excellence Group, reference 04537/GERM/06, funded by the Seneca Foundation (Murcia, Spain). P.D.V. acknowledges CSIC and the Spanish Ministry of Economy and Competitiveness for his “Ramon y Cajal” research contract, co-financed with FEDER funds.Peer reviewe

INVENIO: semantic search based on Google technologies

INVENIO es un sistema que dota de la capacidad de búsqueda semántica sobre la plataforma Google Search Appliance de Google. Es un sistema basado en tecnologías de la Web Semántica y que transforma las consultas realizadas por el usuario en una nueva que obtendrá mejores resultados, pues el sistema es capaz de determinar qué conceptos busca el usuario y traducir esa información a una nueva consulta que se sabe aumentará la satisfacción del cliente.INVENIO is a system that brings semantic search capabilities to Google Search Appliance. The system transforms keywords queries to new ones that will obtain better results by using Web Semantic technologies, so it is able to find out which concepts the user is looking up and translate that knowledge into a new query it is known it will increase user satisfaction

Evaluación de modelos basados en Transformers para el sistema de recuperación de puntuación y mayúsculas en Catalán y Gallego

In recent years, the performance of Automatic Speech Recognition systems (ASR) has increased considerably due to new deep learning methods. However, the raw output of an ASR system consists of a sequence of words without capital letters and punctuation marks. Therefore, a capitalization and punctuation restoration system are one of the most important post-processes of ASR to improve readability and to enable the subsequent use of these results in other NLP models. Most models focus solely on English punctuation resolution, and recently new models of Spanish punctuation restoration have emerged. However, none focus on capitalization and punctuation restoration in Galician and Catalan. In this sense, we propose a system for capitalization and punctuation restoration based on Transformers models for Catalan and Galician. Both models perform very well, with an overall performance of 90.2% for Galician and 90.86% for Catalan, and have the ability to identify proper names, country names, and organizations for uppercase restoration.En los últimos años, el rendimiento de sistemas de Reconocimiento Automático del habla ha aumentado considerablemente gracias a nuevos métodos de deep learning. Sin embargo, la salida bruta de estos sistemas consiste en secuencias de palabras sin mayúsculas ni signos de puntuación. Recuperar esta información mejora la legibilidad y permite su posterior uso en otros modelos de PLN. La mayoría de las soluciones existentes se centran únicamente en inglés; aunque recientemente han surgido nuevos modelos de restauración de la puntuación en español. Sin embargo, ninguno se centra en gallego y catalán. En este sentido, proponemos un sistema de restauración de mayúsculas y puntuación basado en modelos Transformers para estos idiomas. Ambos modelos tienen un rendimiento muy bueno: 90,2% para el gallego y 90,86% para el catalán. Además, también tienen la capacidad de identificar nombres propios, nombres de países y organizaciones para la restauración de mayúsculas.This work is part of the research project (2021/C005/00150076) funded by Spanish Government - Ministerio de Asuntos Económicos y Transformación and by the European Union NextGenerationEU/PRTR. This work is also part of the research project LaTe4PSP (PID2019-107652RB-I00/AEI/ 10.13039/501100011033) funded by MCIN/AEI/10.13039/501100011033

INVOCA: querying the linked open data in natural language

La finalidad del proyecto INVOCA, es el desarrollo de una interfaz de consulta en lenguaje natural para acceder a las distintas bases de conocimiento disponibles actualmente en la Web mediante la ”Linked Open Data”. Este proyecto ha sido desarrollado conjuntamente por la empresa VOCALI y el grupo TECNOMOD de la Universidad de Murcia.The main objective of the INVOCA project is the development of a natural language interface to query the knowledge bases available on the Internet in the Linked Open Data. This project has been developed between the VOCALI enterprise and the TECNOMOD research group of the University of Murcia.Este proyecto ha sido financiado por el Instituto de Fomento de la Región de Murcia ref:2008.03.ID+I.0034

AN ONTOLOGICAL SYSTEM FOR SUPPORTING EVALUATION PROCESSES

Proceedings of the II International Conference on Multimedia and Information & Communication Technologies in Education m-ICTE2003 www.formatex.org/micte2003/micte2003.ht

Discovering HIV related information by means of association rules and machine learning

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

COVID-19 in hospitalized HIV-positive and HIV-negative patients : A matched study

CatedresObjectives: We compared the characteristics and clinical outcomes of hospitalized individuals with COVID-19 with [people with HIV (PWH)] and without (non-PWH) HIV co-infection in Spain during the first wave of the pandemic. Methods: This was a retrospective matched cohort study. People with HIV were identified by reviewing clinical records and laboratory registries of 10 922 patients in active-follow-up within the Spanish HIV Research Network (CoRIS) up to 30 June 2020. Each hospitalized PWH was matched with five non-PWH of the same age and sex randomly selected from COVID-19@Spain, a multicentre cohort of 4035 patients hospitalized with confirmed COVID-19. The main outcome was all-cause in-hospital mortality. Results: Forty-five PWH with PCR-confirmed COVID-19 were identified in CoRIS, 21 of whom were hospitalized. A total of 105 age/sex-matched controls were selected from the COVID-19@Spain cohort. The median age in both groups was 53 (Q1-Q3, 46-56) years, and 90.5% were men. In PWH, 19.1% were injecting drug users, 95.2% were on antiretroviral therapy, 94.4% had HIV-RNA < 50 copies/mL, and the median (Q1-Q3) CD4 count was 595 (349-798) cells/μL. No statistically significant differences were found between PWH and non-PWH in number of comorbidities, presenting signs and symptoms, laboratory parameters, radiology findings and severity scores on admission. Corticosteroids were administered to 33.3% and 27.4% of PWH and non-PWH, respectively (P = 0.580). Deaths during admission were documented in two (9.5%) PWH and 12 (11.4%) non-PWH (P = 0.800). Conclusions: Our findings suggest that well-controlled HIV infection does not modify the clinical presentation or worsen clinical outcomes of COVID-19 hospitalization