Semantic resources in pharmacovigilance: a corpus and an ontology for drug-drug interactions

Mención Internacional en el título de doctorNowadays, with the increasing use of several drugs for the treatment of one or more different diseases (polytherapy) in large populations, the risk for drugs combinations that have not been studied in pre-authorization clinical trials has increased. This provides a favourable setting for the occurrence of drug-drug interactions (DDIs), a common adverse drug reaction (ADR) representing an important risk to patients safety, and an increase in healthcare costs. Their early detection is, therefore, a main concern in the clinical setting. Although there are different databases supporting healthcare professionals in the detection of DDIs, the quality of these databases is very uneven, and the consistency of their content is limited. Furthermore, these databases do not scale well to the large and growing number of pharmacovigilance literature in recent years. In addition, large amounts of current and valuable information are hidden in published articles, scientific journals, books, and technical reports. Thus, the large number of DDI information sources has overwhelmed most healthcare professionals because it is not possible to remain up to date on everything published about DDIs. Computational methods can play a key role in the identification, explanation, and prediction of DDIs on a large scale, since they can be used to collect, analyze and manipulate large amounts of biological and pharmacological data. Natural language processing (NLP) techniques can be used to retrieve and extract DDI information from pharmacological texts, supporting researchers and healthcare professionals on the challenging task of searching DDI information among different and heterogeneous sources. However, these methods rely on the availability of specific resources providing the domain knowledge, such as databases, terminological vocabularies, corpora, ontologies, and so forth, which are necessary to address the Information Extraction (IE) tasks. In this thesis, we have developed two semantic resources for the DDI domain that make an important contribution to the research and development of IE systems for DDIs. We have reviewed and analyzed the existing corpora and ontologies relevant to this domain, based on their strengths and weaknesses, we have developed the DDI corpus and the ontology for drug-drug interactions (named DINTO). The DDI corpus has proven to fulfil the characteristics of a high-quality gold-standard, and has demonstrated its usefulness as a benchmark for the training and testing of different IE systems in the SemEval-2013 DDIExtraction shared task. Meanwhile, DINTO has been used and evaluated in two different applications. Firstly, it has been proven that the knowledge represented in the ontology can be used to infer DDIs and their different mechanisms. Secondly, we have provided a proof-of-concept of the contribution of DINTO to NLP, by providing the domain knowledge to be exploited by an IE pilot prototype. From these results, we believe that these two semantic resources will encourage further research into the application of computational methods to the early detection of DDIs. This work has been partially 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] and by the European Commission Seventh Framework Programme under TrendMiner project [FP7-ICT287863].Hoy en día ha habido un notable aumento del número de pacientes polimedicados que reciben simultáneamente varios fármacos para el tratamiento de una o varias enfermedades. Esta situación proporciona el escenario ideal para la prescripción de combinaciones de fármacos que no han sido estudiadas previamente en ensayos clínicos, y puede dar lugar a un aumento de interacciones farmacológicas (DDIs por sus siglas en inglés). Las interacciones entre fármacos son un tipo de reacción adversa que supone no sólo un riesgo para los pacientes, sino también una importante causa de aumento del gasto sanitario. Por lo tanto, su detección temprana es crucial en la práctica clínica. En la actualidad existen diversos recursos y bases de datos que pueden ayudar a los profesionales sanitarios en la detección de posibles interacciones farmacológicas. Sin embargo, la calidad de su información varía considerablemente de unos a otros, y la consistencia de sus contenidos es limitada. Además, la actualización de estos recursos es difícil debido al aumento que ha experimentado la literatura farmacológica en los últimos años. De hecho, mucha información sobre DDIs se encuentra dispersa en artículos, revistas científicas, libros o informes técnicos, lo que ha hecho que la mayoría de los profesionales sanitarios se hayan visto abrumados al intentar mantenerse actualizados en el dominio de las interacciones farmacológicas. La ingeniería informática puede representar un papel fundamental en este campo permitiendo la identificación, explicación y predicción de DDIs, ya que puede ayudar a recopilar, analizar y manipular grandes cantidades de datos biológicos y farmacológicos. En concreto, las técnicas del procesamiento del lenguaje natural (PLN) pueden ayudar a recuperar y extraer información sobre DDIs de textos farmacológicos, ayudando a los investigadores y profesionales sanitarios en la complicada tarea de buscar esta información en diversas fuentes. Sin embargo, el desarrollo de estos métodos depende de la disponibilidad de recursos específicos que proporcionen el conocimiento del dominio, como bases de datos, vocabularios terminológicos, corpora u ontologías, entre otros, que son necesarios para desarrollar las tareas de extracción de información (EI). En el marco de esta tesis hemos desarrollado dos recursos semánticos en el dominio de las interacciones farmacológicas que suponen una importante contribución a la investigación y al desarrollo de sistemas de EI sobre DDIs. En primer lugar hemos revisado y analizado los corpora y ontologías existentes relevantes para el dominio y, en base a sus potenciales y limitaciones, hemos desarrollado el corpus DDI y la ontología para interacciones farmacológicas DINTO. El corpus DDI ha demostrado cumplir con las características de un estándar de oro de gran calidad, así como su utilidad para el entrenamiento y evaluación de distintos sistemas en la tarea de extracción de información SemEval-2013 DDIExtraction Task. Por su parte, DINTO ha sido utilizada y evaluada en dos aplicaciones diferentes. En primer lugar, hemos demostrado que esta ontología puede ser utilizada para inferir interacciones entre fármacos y los mecanismos por los que ocurren. En segundo lugar, hemos obtenido una primera prueba de concepto de la contribución de DINTO al área del PLN al proporcionar el conocimiento del dominio necesario para ser explotado por un prototipo de un sistema de EI. En vista de estos resultados, creemos que estos dos recursos semánticos pueden estimular la investigación en el desarrollo de métodos computaciones para la detección temprana de DDIs. Este trabajo ha sido financiado parcialmente por el Gobierno Regional de Madrid a través de la red de investigación MA2VICMR [S2009/TIC-1542], por el Ministerio de Educación Español, a través del proyecto MULTIMEDICA [TIN2010-20644-C03-01], y por el Séptimo Programa Macro de la Comisión Europea a través del proyecto TrendMiner [FP7-ICT287863].This work has been partially 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] and by the European Commission Seventh Framework Programme under TrendMiner project [FP7-ICT287863].Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Asunción Gómez Pérez.- Secretario: María Belén Ruiz Mezcua.- Vocal: Mariana Neve

Adquisición de valores en torno al amor romántico o la educación afectivo-sexual en la población joven a partir de series juveniles actuales. Análisis de Skam, España y Sex Education

Este trabajo es una iniciación para analizar los valores que adquiere la población juvenil en temas de amor romántico y educación afectivo sexual Por ello, ¿Existe una buena educación sexual en el sector juvenil? La realidad es que no se sabe demasiado al respecto; los medios de comunicación, el cine y la música que se dirigen a los jóvenes y que conllevan una temática sexual van cargados de estereotipos incluso, a veces, de una información poco adecuada y pertinente para su educación sexual. En la etapa juvenil, las relaciones y la sexualidad adquieren gran importancia en todos los ámbitos, ya que tanto las conductas como los comportamientos constituyen una fuente de valor añadido en el conocimiento, la comprensión, el desarrollo de la identidad y crecimiento personal. Además, según van creciendo y desarrollándose, los y las adolescentes adquieren unos valores a partir de las vivencias y experiencias, así como de los referentes en los que tengan presentes en esta etapa. Ante esto, nos preguntamos: ¿Las carencias sobre temas de educación afectivo sexual y relaciones en la adolescencia son factores clave para que recurran a otro tipo de fuentes de información menos adecuadas?This work is an initiation to analyze the values acquired by the youth population in issues of romantic love and affective sexual education. Therefore, is there a good sexual education in the youth sector? The reality is that not much is known about it; the media, cinema and music that are directed at young people and that involve a sexual theme are loaded with stereotypes including, at times, inadequate and pertinent information for their sexual education. In the youth stage, relationships and sexuality acquire great importance in all areas, since both behaviors and behaviors constitute a source of added value in knowledge understanding, identity development and personal growth. In addition, as they grow and develop, adolescents acquire values from experiences and experiences, as well as from the references in which they are present and this stage. Given this, we ask ourselves: Are adolescents defiencies on issues of affective sexual education and relationships key factors for them to resort to other less adequate sources of information?Departamento de Sociología y Trabajo SocialDepartamento de Sociología y Trabajo SocialMáster en Psicopedagogí

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

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

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

The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions

The management of drug-drug interactions (DDIs) is a critical issue resulting from the overwhelming amount of information available on them. Natural Language Processing (NLP) techniques can provide an interesting way to reduce the time spent by healthcare professionals on reviewing biomedical literature. However, NLP techniques rely mostly on the availability of the annotated corpora. While there are several annotated corpora with biological entities and their relationships, there is a lack of corpora annotated with pharmacological substances and DDIs. Moreover, other works in this field have focused in pharmacokinetic (PK) DDIs only, but not in pharmacodynamic (PD) DDIs. To address this problem, we have created a manually annotated corpus consisting of 792 texts selected from the DrugBank database and other 233 Medline abstracts. This fined-grained corpus has been annotated with a total of 18,502 pharmacological substances and 5028 DDIs, including both PK as well as PD interactions. The quality and consistency of the annotation process has been ensured through the creation of annotation guidelines and has been evaluated by the measurement of the inter-annotator agreement between two annotators. The agreement was almost perfect (Kappa up to 0.96 and generally over 0.80), except for the DDIs in the MedLine database (0.55-0.72). The DDI corpus has been used in the SemEvaI 2013 DDIExtraction challenge as a gold standard for the evaluation of information extraction techniques applied to the recognition of pharmacological substances and the detection of DDIs from biomedical texts. DDIExtraction 2013 has attracted wide attention with a total of 14 teams from 7 different countries. For the task of recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was F1 of 65.1%.Funding: This work was supported by the EU project TrendMiner [FP7-ICT287863], by the project MULTIMEDICA [TIN2010- 20644-C03-01], and by the Research Network MA2VICMR [S2009/TIC-1542].Publicad

Application of machine learning in knowledge discovery for pharmaceutical drug-drug interactions

Abstract. Artificial neural networks (ANNs) have been developed to predict the clinical significance of drug-drug interactions (DDIs) for a set of 35 pharmaceutical drugs using data compiled from the Web-based resources, Lexicomp® and Vidal®, with inputs furnished by various drug pharmacokinetic (PK) and/or pharmacodynamic (PD) properties, and/or drug-enzyme interaction data. Success in prediction of DDI significance was found to vary according to the drug properties used as ANN input, and also varied with the DDI dataset used in training. The Lexicomp® dataset is found to give predictions marginally better than those obtained using the Vidal® dataset, with the best prediction of minor DDIs achieved using a multi-layer perceptron (MLP) model trained using enzyme variables alone (F1 82%) and the best prediction of major DDIs achieved using a MLP model trained on PK/PD properties alone (F1 54%). Given a more comprehensive and more consistent dataset of DDI data, we conclude that machine learning tools could be used to acquire new knowledge on DDIs, and could thus facilitate the regulatory agencies' pharmocovigilance of newly licensed drugs

An ontology for drug-drug interactions

Proceedings of: The 6th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2013). Took place 2013, December 11-12, in Edinburgh, UK. The evnt Web site http://www.swat4ls.org/workshops/edinburgh2013/Drug-drug interactions form a significant risk group for adverse effects associ-ated with pharmaceutical treatment. These interactions are often reported in the literature, however, they are sparsely represented in machine-readable re-sources, such as online databases, thesauri or ontologies. These knowledge sources play a pivotal role in Natural Language Processing (NLP) systems since they provide a knowledge representation about the world or a particular do-main. While ontologies for drugs and their effects have proliferated in recent years, there is no ontology capable of describing and categorizing drug-drug in-teractions. Moreover, there is no artifact that represents all the possible mecha-nisms that can lead to a DDI. To fill this gap we propose DINTO, an ontology for drug-drug interactions and their mechanisms. In this paper we describe the classes, relationships and overall structure of DINTO. The ontology is free for use and available at https://code.google.com/p/dinto/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] and by the European Commission Seventh Framework Programme under the project TrendMiner_Enlarged (EU FP7-ICT 612336).Publicad

Minocycline and the risk of acute psychiatric events in adolescence: A self-controlled case series

BACKGROUND: Minocycline has neurological anti-inflammatory properties and has been hypothesised to have antipsychotic effects. AIM: The aim of this study was to investigate, using routinely collected United Kingdom primary health care data, whether adolescent men and women are more or less likely to receive an urgent psychiatric referral during treatment for acne with minocycline compared with periods of non-treatment. METHOD: A self-controlled case series using United Kingdom Clinical Practice Research Datalink to calculate the incidence rate ratio of urgent psychiatric referrals for individuals, comparing periods during which minocycline was prescribed with unexposed periods, adjusted for age. RESULTS: We found 167 individuals who were at the time exposed to minocycline for a mean of 99 days and who received an urgent psychiatric referral. There was no difference in psychiatric referral risk during periods of exposure compared with periods of non-exposure: incidence rate ratio first 6 weeks of exposure 1.96, 95% confidence interval 0.82-4.71, p=0.132; incidence rate ratio remaining exposure period=1.97, 95% confidence interval 0.86-4.47, p=0.107. CONCLUSIONS: We found no evidence in support of a protective effect of minocycline against severe psychiatric symptoms in adolescence

BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction

Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93 percentage points for drug-drug interactions (DDI corpus), 4.99 percentage points for phenotype-gene relations (PGR corpus), and 2.21 percentage points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiOnt.Comment: ECIR 202