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

Publication date: Available online 20 September 2019Source: Journal of Biomedical InformaticsAuthor(s): Víctor Suárez-Paniagua, Renzo M. Rivera Zavala, Isabel Segura-Bedmar, Paloma MartínezAbstractThis 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 reso...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research