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

publication date

  • November 2019

start page

  • 103285-1

end page

  • 103285-12

volume

  • 99

International Standard Serial Number (ISSN)

  • 1532-0464

abstract

  • 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...

keywords

  • name entity recognition; relation extraction; deep learning; health documents