Abstract Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2 VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method.
Before deep learning research became popular, most relation classification tasks used statistical machine learning methods. Many researchers in the general and medical domains focused on feature-based and kernel-based methods @cite_21 @cite_19 @cite_7 @cite_14 @cite_12 @cite_31 , which are limited by conditions such as manual feature engineering and dependence on existing NLP toolkits.
@cite_14 @cite_7 @cite_21 @cite_19 @cite_31 @cite_12
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This paper addresses an information-extraction problem that aims to identify semantic relations among medical concepts (problems, tests, and treatments) in clinical text. The objectives of the paper are twofold. First, we extend an earlier one-page description (appearing as a part of [5]) of a top-ranked model in the 2010 I2B2 NLP Challenge to a necessary level of details, with the belief that feature design is the most crucial factor to the success of our system and hence deserves a more detailed discussion. We present a precise quantification of the contributions of a wide variety of knowledge sources. In addition, we show the end-to-end results obtained on the noisy output of a top-ranked concept detector, which could help construct a more complete view of the state of the art in the real-world scenario. As the second major objective, we reformulate our models into a composite-kernel framework and present the best result, according to our knowledge, on the same dataset. Objective A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records. @PARASPLIT Materials and methods A single support vector machine classifier was used to identify relations between concepts and to assign their semantic type. Several resources such as Wikipedia, WordNet, General Inquirer, and a relation similarity metric inform the classifier. @PARASPLIT Results The techniques reported in this paper were evaluated in the 2010 i2b2 Challenge and obtained the highest F1 score for the relation extraction task. When gold standard data for concepts and assertions were available, F1 was 73.7, precision was 72.0, and recall was 75.3. F1 is defined as 2 *Precision *Recall (Precision+Recall). Alternatively, when concepts and assertions were discovered automatically, F1 was 48.4, precision was 57.6, and recall was 41.7. @PARASPLIT Discussion Although a rich set of features was developed for the classifiers presented in this paper, little knowledge mining was performed from medical ontologies such as those found in UMLS. Future studies should incorporate features extracted from such knowledge sources, which we expect to further improve the results. Moreover, each relation discovery was treated independently. Joint classification of relations may further improve the quality of results. Also, joint learning of the discovery of concepts, assertions, and relations may also improve the results of automatic relation extraction. @PARASPLIT Conclusion Lexical and contextual features proved to be very important in relation extraction from medical texts. When they are not available to the classifier, the F1 score decreases by 3.7 . In addition, features based on similarity contribute to a decrease of 1.1 when they are not available. We present a novel approach to relation extraction, based on the observation that the information required to assert a relationship between two named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency graph. Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels. This paper describes our system for SemEval-2010 Task 8 on multi-way classification of semantic relations between nominals. First, the type of semantic relation is classified. Then a relation type-specific classifier determines the relation direction. Classification is performed using SVM classifiers and a number of features that capture the context, semantic role affiliation, and possible pre-existing relations of the nominals. This approach achieved an F1 score of 82.19 and an accuracy of 77.92 . Abstract Integrating semantic features into parse trees is an active research topic in open-domain natural language processing (NLP). We study six different parse tree structures enriched with various semantic features for determining entity relations in clinical notes using a tree kernel-based relation extraction system. We used the relation extraction task definition and the dataset from the popular 2010 i2b2 VA challenge for our evaluation. We found that the parse tree structure enriched with entity type suffixes resulted in the highest F1 score of 0.7725 and was the fastest. In terms of reducing the number of feature vectors in trained models, the entity type feature was most effective among the semantic features while adding semantic feature node was better than adding feature suffixes to the labels. Our study demonstrates that parse tree enhancements with semantic features are effective for clinical relation extraction. Despite the successes of distant supervision approaches to relation extraction in the news domain, the lack of a comprehensive ontology of medical relations makes it difficult to apply such approaches to relation classification in the medical domain. In light of this difficulty, we propose an ensemble approach to this task where we exploit human-supplied knowledge to guide the design of members of the ensemble. Results on the 2010 i2b2 VA Challenge corpus show that our ensemble approach yields a 19.8 relative error reduction over a state-of-the-art baseline.