IHS-RD-Belarus: Identification and Normalization of Disorder Concepts in Clinical Notes
نویسندگان
چکیده
This paper describes clinical disorder recognition and encoding system submitted by IHS R&D Belarus team at the SemEval-2015 shared task related to analysis of clinical texts. Our system is based on IHS Goldfire Linguistic Processor and uses a rich set of lexical, syntactic and semantic features. The proposed system consists of two components: a CRF-based approach to recognize disorder entities and empirical ranking to encode disorders to UMLS CUIs. Evaluation on the test data set showed that our system achieved the F-measure of 0.898 for entity recognition and the F-measure of 0.794 for UMLS CUI. The combined score for whole task is 0.690 (rank 17 out of 40 submissions).
منابع مشابه
IHS-RD-BELARUS: Clinical Named Entities Identification in French Medical Texts
In this paper we present the results of our participation in the Task 1b of the 2015 CLEFeHealth challenge, whose goal was the identification of clinical entities of various types from medical texts in French and its normalization. We used the CRF-based system developed for disorder recognition in English and enhanced with French knowledge resources to recognize 10 types of clinic named entitie...
متن کاملIHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity
This paper describes the system for rating the degree of semantic equivalence between two text snippets developed by IHS-RD-Belarus for the SemEval 2016 STS shared task (Task 1). To predict the human ratings of text similarity we use a support vector regression model with multiple features representing similarity and difference scores calculated for each
متن کاملIHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence
This paper describes the system submitted by IHS-RD-Belarus team for the sentiment detection polarity subtask on Aspect Based Sentiment Analysis task at the SemEval 2016 workshop on semantic evaluation. We developed a system based on artificial neural network to detect the sentiment polarity of opinions. Evaluation on the test data set showed that our system achieved the F-score of 0.83 for res...
متن کاملBioinformaticsUA: Machine Learning and Rule-Based Recognition of Disorders and Clinical Attributes from Patient Notes
Natural language processing and text analysis methods offer the potential of uncovering hidden associations from large amounts of unprocessed texts. The SemEval-2015 Analysis of Clinical Text task aimed at fostering research on the application of these methods in the clinical domain. The proposed task consisted of disorder identification with normalization to SNOMED-CT concepts, and disorder at...
متن کاملIHS_RD: Lexical Normalization for English Tweets
This paper describes the Twitter lexical normalization system submitted by IHS R&D Belarus team for the ACL 2015 workshop on noisy user-generated text. The proposed system consists of two components: a CRFbased approach to identify possible normalization candidates, and a post-processing step in an attempt to normalize words that do not have normalization variants in the lexicon. Evaluation on ...
متن کامل