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

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

Position-aware deep multi-task learning for drug–drug interaction extraction

Objective A drug–drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications. Therefore, automatically extracting DDIs from biomedical texts is sorely needed. Methods and material In this paper, we propose a novel position-aware deep multi-task learning approach for extracting DDIs from biomedical texts. In particular, sentences are represented as a sequence of word embeddings and position embeddings. An attention-based bidirectional long short-term memory (BiLSTM) network is used to encode each sentence. The relative position information of words with the target drugs in text is combined with the hidden states of BiLSTM to generate the position-aware attention weights. Moreover, the tasks of predicting whether or not two drugs interact with each other and further distinguishing the types of interactions are learned jointly in multi-task learning framework. Results The proposed approach has been evaluated on the DDIExtraction challenge 2013 corpus and the results show that with the position-aware attention only, our proposed approach outperforms the state-of-the-art method by 0.99% for binary DDI classification, and with both position-aware attention and multi-task learning, our approach achieves a micro F-score of 72.99% on interaction type identification, outperforming the state-of-the-art approach by 1.51%, which demonstrates the effectiveness of the proposed approach

Using a shallow linguistic kernel for drug-drug interaction extraction

A drug–drug interaction (DDI) occurs when one drug influences the level or activity of another drug. Information Extraction (IE) techniques can provide health care professionals with an interesting way to reduce time spent reviewing the literature for potential drug–drug interactions. Nevertheless, no approach has been proposed to the problem of extracting DDIs in biomedical texts. In this article, we study whether a machine learning-based method is appropriate for DDI extraction in biomedical texts and whether the results provided are superior to those obtained from our previously proposed pattern-based approach [1]. The method proposed here for DDI extraction is based on a supervised machine learning technique, more specifically, the shallow linguistic kernel proposed in Giuliano et al. (2006) [2]. Since no benchmark corpus was available to evaluate our approach to DDI extraction, we created the first such corpus, DrugDDI, annotated with 3169 DDIs. We performed several experiments varying the configuration parameters of the shallow linguistic kernel. The model that maximizes the F-measure was evaluated on the test data of the DrugDDI corpus, achieving a precision of 51.03%, a recall of 72.82% and an F-measure of 60.01%. To the best of our knowledge, this work has proposed the first full solution for the automatic extraction of DDIs from biomedical texts. Our study confirms that the shallow linguistic kernel outperforms our previous pattern-based approach. Additionally, it is our hope that the DrugDDI corpus will allow researchers to explore new solutions to the DDI extraction problem.This study was funded by the Projects MA2VICMR (S2009/TIC-1542) and MULTIMEDICA (TIN2010-20644-C03-01).Publicad

Aplicación de técnicas de aprendizaje automático para la extracción de información en textos farmacológicos

En la actualidad los profesionales del dominio biomédico necesitan tener información actualizada de su campo para llevar a cabo su trabajo de manera fiable y profesional. Dentro del dominio biomédico, la administración de fármacos requiere saber de antemano si dos fármacos interaccionan entre sí, ya que esta interacción puede provocar efectos no deseados en la salud del paciente. Los profesionales cuentan con ingentes cantidades de información, ya sea en textos biomédicos no estructurados o en bases de datos; es por esto que se necesita un método automático para extraer información de estas fuentes de datos para poder detectar interacciones entre fármacos. En este proyecto se van a estudiar distintas técnicas de aprendizaje automático supervisado para detectar posibles interacciones entre dos fármacos. Partiendo del corpus DrugDDI, creado en la tesis Application of Information Extraction techniques to pharmacological domain: Extracting drug-drug interactions, se van a aplicar diferentes algoritmos para su posterior estudio y comparación con los resultados obtenidos en dicha tesis. ____________________________________________________________________________________________________________________________In the biomedical domain, interaction between two or more drugs is a desired knowing in drugs administration, as that interaction can provoke undesirable effects over a patient health. Medical professional have access to huge amounts of data, whether they are in biomedical unstructured texts or in databases. For this reason it is desirable an automatic method to extract useful information from this data sources for processing and detecting drugs interactions. In this project we are going to introduce some supervised machine learning techniques in order to detect possible interactions between two drugs. Based on the DrugDDI corpus, gathered in the thesis Application of Information Extraction techniques to pharmacological domain: Extracting drug-drug interactions, we are going to apply different algorithms for its later research and comparison with the results obtained in the thesis.Ingeniería Técnica en Informática de Gestió