A two-stage deep learning approach for extracting entities and relationships from medical texts

This Work Presents A Two-Stage Deep Learning System For Named Entity Recognition (Ner) And Relation Extraction (Re) From Medical Texts. These Tasks Are A Crucial Step To Many Natural Language Understanding Applications In The Biomedical Domain. Automatic Medical Coding Of Electronic Medical Records, Automated Summarizing Of Patient Records, Automatic Cohort Identification For Clinical Studies, Text Simplification Of Health Documents For Patients, Early Detection Of Adverse Drug Reactions Or Automatic Identification Of Risk Factors Are Only A Few Examples Of The Many Possible Opportunities That The Text Analysis Can Offer In The Clinical Domain. In This Work, Our Efforts Are Primarily Directed Towards The Improvement Of The Pharmacovigilance Process By The Automatic Detection Of Drug-Drug Interactions (Ddi) From Texts. Moreover, We Deal With The Semantic Analysis Of Texts Containing Health Information For Patients. Our Two-Stage Approach Is Based On Deep Learning Architectures. Concretely, Ner Is Performed Combining A Bidirectional Long Short-Term Memory (Bi-Lstm) And A Conditional Random Field (Crf), While Re Applies A Convolutional Neural Network (Cnn). Since Our Approach Uses Very Few Language Resources, Only The Pre-Trained Word Embeddings, And Does Not Exploit Any Domain Resources (Such As Dictionaries Or Ontologies), This Can Be Easily Expandable To Support Other Languages And Clinical Applications That Require The Exploitation Of Semantic Information (Concepts And Relationships) From Texts...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)

Resumen de TASS 2018: Opiniones, Salud y Emociones

This is an overview of the Workshop on Semantic Analysis at the SEPLN congress held in Sevilla, Spain, in September 2018. This forum proposes to participants four different semantic tasks on texts written in Spanish. Task 1 focuses on polarity classification; Task 2 encourages the development of aspect-based polarity classification systems; Task 3 provides a scenario for discovering knowledge from eHealth documents; finally, Task 4 is about automatic classification of news articles according to safety. The former two tasks are novel in this TASS's edition. We detail the approaches and the results of the submitted systems of the different groups in each task.Este artículo ofrece un resumen sobre el Taller de Análisis Semántico en la SEPLN (TASS) celebrado en Sevilla, España, en septiembre de 2018. Este foro propone a los participantes cuatro tareas diferentes de análisis semántico sobre textos en español. La Tarea 1 se centra en la clasificación de la polaridad; la Tarea 2 anima al desarrollo de sistemas de polaridad orientados a aspectos; la Tarea 3 consiste en descubrir conocimiento en documentos sobre salud; finalmente, la Tarea 4 propone la clasificación automática de noticias periodísticas según un nivel de seguridad. Las dos últimas tareas son nuevas en esta edición. Se ofrece una síntesis de los sistemas y los resultados aportados por los distintos equipos participantes, así como una discusión sobre los mismos.This work has been partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER), the projects REDES (TIN2015-65136-C2-1-R, TIN2015-65136-C2-2-R) and SMART-DASCI (TIN2017-89517-P) from the Spanish Government, and “Plataforma Inteligente para Recuperación, Análisis y Representación de la Información Generada por Usuarios en Internet” (GRE16-01) from University of Alicante. Eugenio Martínez Cámara was supported by the Spanish Government Programme Juan de la Cierva Formación (FJCI-2016-28353)

Deep learning in clinical natural language processing: a methodical review.

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research. MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers. RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) and word2vec embeddings (74.1%) were the most popular methods; the information extraction tasks of text classification, named entity recognition, and relation extraction were dominant (89.2%). However, there was a long tail of other methods and specific tasks. Most contributions were methodological variants or applications, but 20.8% were new methods of some kind. The earliest adopters were in the NLP community, but the medical informatics community was the most prolific. DISCUSSION: Our analysis shows growing acceptance of deep learning as a baseline for NLP research, and of DL-based NLP in the medical community. A number of common associations were substantiated (eg, the preference of recurrent neural networks for sequence-labeling named entity recognition), while others were surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). CONCLUSION: Deep learning has not yet fully penetrated clinical NLP and is growing rapidly. This review highlighted both the popular and unique trends in this active field