Challenges for automatically extracting molecular interactions from full-text articles

<p>Abstract</p> <p>Background</p> <p>The increasing availability of full-text biomedical articles will allow more biomedical knowledge to be extracted automatically with greater reliability. However, most Information Retrieval (IR) and Extraction (IE) tools currently process only abstracts. The lack of corpora has limited the development of tools that are capable of exploiting the knowledge in full-text articles. As a result, there has been little investigation into the advantages of full-text document structure, and the challenges developers will face in processing full-text articles.</p> <p>Results</p> <p>We manually annotated passages from full-text articles that describe interactions summarised in a Molecular Interaction Map (MIM). Our corpus tracks the process of identifying facts to form the MIM summaries and captures any factual dependencies that must be resolved to extract the fact completely. For example, a fact in the results section may require a synonym defined in the introduction. The passages are also annotated with negated and coreference expressions that must be resolved.</p> <p>We describe the guidelines for identifying relevant passages and possible dependencies. The corpus includes 2162 sentences from 78 full-text articles. Our corpus analysis demonstrates the necessity of full-text processing; identifies the article sections where interactions are most commonly stated; and quantifies the proportion of interaction statements requiring coherent dependencies. Further, it allows us to report on the relative importance of identifying synonyms and resolving negated expressions. We also experiment with an oracle sentence retrieval system using the corpus as a gold-standard evaluation set.</p> <p>Conclusion</p> <p>We introduce the MIM corpus, a unique resource that maps interaction facts in a MIM to annotated passages within full-text articles. It is an invaluable case study providing guidance to developers of biomedical IR and IE systems, and can be used as a gold-standard evaluation set for full-text IR tasks.</p

Anaphora Resolution in Business Process Requirement Engineering

Anaphora resolution (AR) is one of the most important tasks in natural language processing which focuses on the problem of resolving what a pronoun, or a noun phrase refers to. Moreover, AR plays an essential role when dealing with business process textual description, either when trying to discover the process model from the text, or when validating an existing model. It helps these systems in discovering the core components in any process model (actors and objects).In this paper, we propose a domain specific AR system. The approach starts by automatically generating the concept map of the text, then the system uses this map to resolve references using the syntactic and semantic relations in the concept map. The approach outperforms the state-of-the art performance in the domain of business process texts with more than 73% accuracy. In addition, this approach could be easily adopted to resolve references in other domains

Accelerating COVID-19 research with graph mining and transformer-based learning

In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and the transformer model. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone

ANAPHORA RESOLUTION BASED ON SEMANTIC RELATEDNESS IN THE BIOMEDICAL DOMAIN

In Linguistics, an anaphor is an expression whose interpretation depends upon another expression in context, namely an antecedent expression. Anaphora resolution is a task of identifying the anaphorical relation between the anaphor and its antecedent. Anaphora resolution is used in many high-level tasks of Natural Language Processing. Traditionally, the rule-based approaches to anaphora resolution rely on the syntactic structures and discourse features. In my study, I implement two semantic approaches on biomedical texts, ontology-dependent method and ontology-independent vector semantic method. The ontology-dependent method will be used to locate the antecedent for noun phrases with determiners while the ontology-independent method will be implemented on pronouns. The results show that the semantic approaches are promising directions in investigating resolutions for anaphora problems in the future.Master of Art

Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

BACKGROUND: Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs. OBJECTIVE: We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps. METHODS: We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively. RESULTS: Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance. CONCLUSIONS: Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning

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)

Coreference resolution on entities and events for hospital discharge summaries

Includes bibliographical references (p. 76-80).Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.The wealth of medical information contained in electronic medical records (EMRs) and Natural Language Processing (NLP) technologies that can automatically extract information from them have opened the doors to automatic patient-care quality monitoring and medical- assist question answering systems. This thesis studies coreference resolution, an information extraction (IE) subtask that links together specific mentions to each entity. Coreference resolution enables us to find changes in the state of entities and makes it possible to answer questions regarding the information thus obtained. We perform coreference resolution on a specific type of EMR, the hospital discharge summary. We treat coreference resolution as a binary classification problem. Our approach yields insights into the critical features for coreference resolution for entities that fall into five medical semantic categories that commonly appear in discharge summaries.by Tian Ye He.M.Eng