Multiple features for clinical relation extraction: A machine learning approach Abstract   Relation extraction aims to discover relational facts about entity mentions from plain texts. In this work, we focus on clinical relation extraction; namely, given a medical record with mentions of drugs and their attributes, we identify relations between these entities. We propose a machine learning model with a novel set of knowledgebased and BioSentVec embedding features. We systematically investigate the impact of these features with standard distance and wordbased features, conducting experiments on two benchmark datasets of clinical texts from MADE 2018 and n2c2 2018 shared tasks. For comparison with the featurebased model, we utilize stateoftheart models and three BERTbased models, including BioBERT and Clinical BERT. Our results demonstrate that distance and word features provide significant benefits to the classifier. Knowledgebased features improve classification results only for particular types of relations. The sentence embedding feature provides the largest improvement in results, among other explored features on the MADE corpus. The classifier obtains stateoftheart performance in clinical relation extraction with Fmeasure of 92.6%, improving Fmeasure by 3.5% on the MADE corpus.

BioRel: A LargeScale Dataset for Biomedical Relation Extraction Valuable biomedical knowledge usually exists in the form of electronic publications and literature, which is growing at an enormous rate. Relation extraction plays a critical role in discovering such knowledge and transform them into structural form. Previous relation extraction datasets in biomedical domain are mainly humanannotated, whose scales are usually limited due to their laborintensive and timeconsuming nature. In this paper, we present BioRel, a largescale dataset constructed by using Unified Medical Language System (UMLS) as knowledge base and Medline as corpus. Entities in sentences of Medline are identified and linked to UMLS by Metamap. Relation label for each sentence is recognized using distant supervision. We adapt both stateoftheart deep learning and statistical machine learning methods as baseline models and conduct comprehensive experiments on BioRel. Experimental results show that BioRel is suitable for training and evaluating relation extraction models for both deep learning and statistical methods by providing both reasonable baseline performance and many remaining challenges.