Comparando enfoques deep learning en una fase y en dos fases para extraer interacciones farmacológicas de texto

Drug-drug interactions (DDI) are a cause of adverse drug reactions. They occur when a drug has an impact on the effect of another drug. There is not a complete, up to date database where health care professionals can consult the interactions of any drug because most of the knowledge on DDI is hidden in unstructured text. In last years, deep learning has been succesfully applied to the extraction of DDI from texts, which requires the detection and later classification of DDI. Most of the deep learning systems for DDI extraction developed so far have addressed the detection and classification in one single step. In this study, we compare the performance of one-stage and two-stage architectures for DDI extraction. Our architectures are based on a bidirectional recurrent neural network layer composed of Gated Recurrent Units. The two-stage system obtained a 67.45 % micro-average F1 score on the test set.Las interacciones farmacológicas (DDI) son una de las causas de reacciones adversas a medicamentos. Ocurren cuando una medicina interfiere en la acción de una segunda. En la actualidad, no existe una base de datos completa y actualizada donde los profesionales de la salud puedan consultar las interacciones de cualquier medicamento porque la mayor parte del conocimiento sobre DDIs está oculto en texto no estructurado. En los últimos años, el aprendizaje profundo se ha aplicado con éxito a la extracción de DDIs de los textos, lo que requiere la detección y posterior clasificación de DDIs. La mayoría de los sistemas de aprendizaje profundo para extracción de DDIs desarrollados hasta ahora han abordado la detección y clasificación en un solo paso. En este estudio, comparamos el rendimiento de las arquitecturas de una y dos etapas para la extracción de DDI. Nuestras arquitecturas se basan en una capa de red neuronal recurrente bidireccional compuesta de Gated Recurrent Units (GRU). El sistema en dos etapas obtuvo un puntaje F1 promedio de 67.45 % en el dataset de evaluación.This work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)

Application of information extraction techniques to pharmacological domain : extracting drug-drug interactions

Una interacción farmacológica ocurre cuando los efectos de un fármaco se modifican por la presencia de otro. Las consecuencias pueden ser perjudiciales si la interacción causa un aumento de la toxicidad del fármaco o la disminución de su efecto, pudiendo provocar incluso la muerte del paciente en los peores casos. Las interacciones farmacológicas no sólo suponen un grave problema para la seguridad del paciente, sino que además también conllevan un importante incremento en el gasto médico. En la actualidad, el personal sanitario tiene a su disposición diversas bases de datos sobre interacciones que permiten evitar posibles interacciones a la hora de prescribir un determinado tratamiento, sin embargo, estas bases de datos no están completas. Por este motivo, médicos y farmacéuticos se ven obligados a revisar una gran cantidad de artículos científicos e informes sobre seguridad de medicamentos para estar al día de todo lo publicado en relación al tema. Desgraciadamente, el gran volumen de información al respecto hace que estos profesionales estén desbordados ante tal avalancha. El desarrollo de métodos automáticos que permitan recopilar, mantener e interpretar toda esta información es crucial a la hora de conseguir una mejora real en la detección temprana de las interacciones entre fármacos. Por tanto, la extracción de información podría reducir el tiempo empleado por el personal médico en la revisión de la literatura médica. Sin embargo, la extracción de interacciones farmacológicas a partir textos biomédicos no ha sido dirigida hasta el momento. Motivados por estos aspectos, en esta tesis hemos realizado un estudio detallado sobre diversas técnicas de extracción de información aplicadas al dominio farmacológico. Basándonos en este estudio, hemos propuesto dos aproximaciones distintas para la extracción de interacciones farmacológicas de los textos. Nuestra primera aproximación propone un enfoque híbrido, que combina análisis sintáctico superficial y la aplicación de patrones léxicos definidos por un farmacéutico. La segunda aproximación se aborda mediante aprendizaje supervisado, concretamente, el uso de métodos kernels. Además, se han desarrollado las siguientes tareas auxiliares: (1) el análisis de los textos utilizando la herramienta UMLS MetaMap Transfer (MMTx), que proporciona información sintáctica y semántica, (2) un proceso para identificar y clasificar los nombres de fármacos que ocurren en los textos, y (3) un proceso para reconoger las expresiones anafóricas que se refieren a fármacos. Un prototipo ha sido desarrollado para integrar y combinar las distintas técnicas propuestas en esta tesis. Para la evaluación de las dos propuestas, con la ayuda de un farmacéutico desarrollamos y anotamos un corpus con interacciones farmacológicas. El corpus DrugDDI es una de las principales aportaciones de la tesis, ya que es el primer corpus en el dominio biomédico anotado con este tipo de información y porque creemos que puede alentar la investigación sobre extracción de información en el dominio farmacológico. Los experimentos realizados demuestran que el enfoque basado en kernels consigue mejores resultados que los reportados por el enfoque que utiliza información sintáctica y patrones léxicos. Además, los kernels consiguen resultados comparables a los obtenidos en dominios similares como son las interacciones entre proteínas. Esta tesis se ha llevado a cabo en el marco del consorcio de investigación MAVIRCM (Mejorando el acceso y visibilidad de la información multilingüe en red para la Comunidad de Madrid, www.mavir.net) dentro del Programa de Actividades de I+D en Tecnologías 2005-2008 de la Comunidad de Madrid (S-0505/TIC-0267) así como en el proyecto de investigación BRAVO: ”Búsqueda de Respuestas Avanzada Multimodal y Multilingüe” (TIN2007-67407-C03-01).----------------------------------------------------------------------------------------A drug-drug interaction occurs when one drug influences the level or activity of another drug. The detection of drug interactions is an important research area in patient safety since these interactions can become very dangerous and increase health care costs. Although there are different databases supporting health care professionals in the detection of drug interactions, this kind of resource is rarely complete. Drug interactions are frequently reported in journals of clinical pharmacology, making medical literature the most effective source for the detection of drug interactions. However, the increasing volume of the literature overwhelms health care professionals trying to keep an up-to-date collection of all reported drug-drug interactions. The development of automatic methods for collecting, maintaining and interpreting this information is crucial for achieving a real improvement in their early detection. Information Extraction (IE) techniques can provide an interesting way of reducing the time spent by health care professionals on reviewing the literature. Nevertheless, no approach has been carried out to extract drug-drug interactions from biomedical texts. In this thesis, we have conducted a detailed study on various IE techniques applied to biomedical domain. Based on this study, we have proposed two different approximations for the extraction of drug-drug interactions from texts. The first approximation proposes a hybrid approach, which combines shallow parsing and pattern matching to extract relations between drugs from biomedical texts. The second approximation is based on a supervised machine learning approach, in particular, kernel methods. In addition, we have created and annotated the first corpus, DrugDDI, annotated with drug-drug interactions, which allow us to evaluate and compare both approximations. To the best of our knowledge, the DrugDDI corpus is the only available corpus annotated for drug-drug interactions and this thesis is the first work which addresses the problem of extracting drug-drug interactions from biomedical texts. We believe the DrugDDI corpus is an important contribution because it could encourage other research groups to research into this problem. We have also defined three auxiliary processes to provide crucial information, which will be used by the aforementioned approximations. These auxiliary tasks are as follows: (1) a process for text analysis based on the UMLS MetaMap Transfer tool (MMTx) to provide shallow syntactic and semantic information from texts, (2) a process for drug name recognition and classification, and (3) a process for drug anaphora resolution. Finally, we have developed a pipeline prototype which integrates the different auxiliary processes. The pipeline architecture allows us to easily integrate these modules with each of the approaches proposed in this thesis: pattern-matching or kernels. Several experiments were performed on the DrugDDI corpus. They show that while the first approximation based on pattern matching achieves low performance, the approach based on kernel-methods achieves a performance comparable to those obtained by approaches which carry out a similar task such as the extraction of protein-protein interactions. This work has been partially supported by the Spanish research projects: MAVIR consortium (S-0505/TIC-0267, www.mavir.net), a network of excellence funded by the Madrid Regional Government and TIN2007-67407-C03-01 (BRAVO: Advanced Multimodal and Multilingual Question Answering)

Exploring the impact of covid-19 on social life by deep learning

Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model.This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the context of “Fostering Young Doctors Research” (NLP4RARE-CM-UC3M), as well as in the context of “Excellence of University Professors” (EPUC3M17) and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

DDIExtractor: A Web-based Java Tool for Extracting Drug-Drug Interactions from Biomedical Texts

Proceeding of: 16th International Conference on Applications of Natural Language to Information Systems, NLDB 201. Took place 2011, June 28-30, in Alicante, Spain. The event Web site is http://gplsi.dlsi.ua.es/congresos/nldb11/A drug-drug interaction (DDIs) occurs when one drug influences the level or activity of another drug. The detection of DDIs is an important research area in patient safety since these interactions can become very dangerous and increase health care costs. Although there are several databases and web tools providing information on DDIs to patients and health-care professionals, these resources are not comprehensive because many DDIs are only reported in the biomedical literature. This paper presents the first tool for detecting drug-drug interactions from biomedical texts called DDIExtractor. The tool allows users to search by keywords in the Medline 2010 baseline database and then detect drugs and DDIs in any retrieved document.This work is supported by the projects MA2VICMR (S2009/TIC-1542) and MULTIMEDICA (TIN2010-20644-C03-01).Publicad

EasyLecto: A lexical simplification system for adverse drug effects in Spanish patient information leaflets

Presentamos EasyLecto, un sistema de simplificación léxica de efectos adversos presentes en prospectos de fármacos en español. El método de simplificación léxica que utiliza EasyLecto se basa en la frecuencia de las palabras para determinar los sinónimos más simples. Este sistema propone los mejores sinónimos y definiciones, obtenidos a partir de los recursos MedlinePlus y MedDRA. El sistema puede ayudar a los lectores con bajo nivel de alfabetización, con problemas cognitivos o discapacidad a la hora de entender los efectos adversos presentes en prospectos de fármacos.We introduce EasyLecto, a lexical simplification system of adverse effects in patient information leaflet in Spanish. The method of lexical simplification using EasyLecto is based on the frequency of words to determine the simplest synonyms. This system proposes the best synonyms and meanings, obtained from the Medline-Plus and MedDRA resources, representing their benefit for readers with low literacy, with cognitive problems or handicapped in understanding the adverse drug effects in patient information leaflet in Spanish.Este trabajo ha sido financiado por el proyecto eGovernAbility-Access (TIN2014-52665-C2-2-R)

An Ontology for formal representation of Drug Drug Interaction Knowledge

The poster at: The Sixth International Biocuration Conference (Biocuration 2013), took at April 7–10, 2013 in Churchill College, Cambridge, UK. The event web site in: http://www.ebi.ac.uk/biocuration2013/content/homeOntologies are useful tools in text miming reserach tasks as a source of specialized vocabulary of terms and relationships in a given domain. Furthermore, formal knowledge representation propvieded by ontologies can be applied for new knowledge inference, wich can be exploited for biomedical research purposes. Drug-drug interactions (DDIs) are common adverse drug ractions having an important impact on patient safety and healthcare cost.This work was supported by the Regional Government of Madrid under the Research Network MA2VICMR [S2009/TIC-1542], by the Spanish Ministry of Education under the project MULTIMEDICA [TIN2010-20644-C03-01].Publicad

The 1st DDIExtraction-2011 Challenge Task: Extraction of Drug-Drug Interactions from Biomedical Texts

Proceeding at: The 1st DDIExtraction-2011 Challenge Task: Extraction of Drug-Drug Interactions from Biomedical Texts. Took place September, 2011, in Huelva (Spain).We present an evaluation task designed to provide a framework for comparing different approaches to extracting drug-drug interactions from biomedical texts.We define the task, describe the training/test data, list the participating systems and discuss their results. There were 10 teams who submitted a total of 40 runs.This study was funded by the projects MA2VICMR (S2009/TIC-1542) and MULTIMEDICA (TIN2010-20644-C03-01). The organizers are particularly grate-ful to all participants who contributed to detect annotation errors in the corpus.Publicad

Annotation Issues in Pharmacological Texts

This works is at: 5th International Conference on Corpus Linguistics (CILC2013), took place 2013, March 14-16, in Alicante (Spain). Conference web site: http://web.ua.es/en/cilc2013/Natural language processing of pharmacological texts includes recognition of drug names and extraction of relationships between them. To this purpose, pharmacological annotated corpora are required. These corpora are usually semantically annotated by domain experts. However, other linguistic aspects should be considered to ensure the quality and consistency of the annotation. This paper introduces several linguistic phenomena affecting the annotation of both drug named entities and drug-drug interaction relationships that arose during the annotation process of the DDI corpus. The detailed documentation of these issues and the decisions on them will improve the quality of the annotated corpus and its usefulness for other researchers or usersThis work was supported by the Regional Government of Madrid under the Research Network MA2VICMR [S2009/TIC-1542] and by the Spanish Ministry of Economy under the project MULTIMEDICA [TIN2010-20644- C03-01].Publicad

Extracting information from radiology reports by Natural Language Processing and Deep Learning

This work was supported by the NLP4RARE-CM-UC3M, which was developed under the Interdisciplinary Projects Program for Young Researchers at University Carlos III of Madrid. The work was also supported by the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation)

SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)

Proceedings of: International Workshop on Semantic Evaluation. SemEval-2013 : Semantic Evaluation Exercises. Took place in 2013 June, 14-15, in Atlanta, Georgia (USA). The event Web site in http://www.cs.york.ac.uk/semeval-2013/The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drugdrug interactions that appear in biomedical literature. We propose two subtasks for the DDIExtraction 2013 Shared Task challenge: 1) the recognition and classification of drug names and 2) the extraction and classification of their interactions. Both subtasks have been very successful in participation and results. There were 14 teams who submitted a total of 38 runs. The best result reported for the first subtask was F1 of 71.5% and 65.1% for the second one.This research work has been supported by the Regional Government of Madrid under the Research Network MA2VICMR (S2009/TIC-1542), by the Spanish Ministry of Education under the project MULTIMEDICA (TIN2010-20644-C03-01).Publicad