A context-blocks model for identifying clinical relationships in patient records

BMC Bioinformatics

Research

Rezarta Islamaj Doğan¹  Aurélie Névéol¹  Zhiyong Lu¹ 

[1] National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA;

关键词: Conditional Random Field;    Unify Medical Language System;    Machine Learning Model;    Biomedical Text;    Concept Recognition;   

DOI  :  10.1186/1471-2105-12-S3-S3

来源: Springer

【 摘 要 】

BackgroundPatient records contain valuable information regarding explanation of diagnosis, progression of disease, prescription and/or effectiveness of treatment, and more. Automatic recognition of clinically important concepts and the identification of relationships between those concepts in patient records are preliminary steps for many important applications in medical informatics, ranging from quality of care to hypothesis generation.MethodsIn this work we describe an approach that facilitates the automatic recognition of eight relationships defined between medical problems, treatments and tests. Unlike the traditional bag-of-words representation, in this work, we represent a relationship with a scheme of five distinct context-blocks determined by the position of concepts in the text. As a preliminary step to relationship recognition, and in order to provide an end-to-end system, we also addressed the automatic extraction of medical problems, treatments and tests. Our approach combined the outcome of a statistical model for concept recognition and simple natural language processing features in a conditional random fields model. A set of 826 patient records from the 4th i2b2 challenge was used for training and evaluating the system.ResultsResults show that our concept recognition system achieved an F-measure of 0.870 for exact span concept detection. Moreover the context-block representation of relationships was more successful (F-Measure = 0.775) at identifying relationships than bag-of-words (F-Measure = 0.402). Most importantly, the performance of the end-to-end system of relationship extraction using automatically extracted concepts (F-Measure = 0.704) was comparable to that obtained using manually annotated concepts (F-Measure = 0.711), and their difference was not statistically significant.ConclusionsWe extracted important clinical relationships from text in an automated manner, starting with concept recognition, and ending with relationship identification. The advantage of the context-blocks representation scheme was the correct management of word position information, which may be critical in identifying certain relationships. Our results may serve as benchmark for comparison to other systems developed on i2b2 challenge data. Finally, our system may serve as a preliminary step for other discovery tasks in medical informatics.

【 授权许可】

CC BY   
© The Author(s) 2011. This article is published under license to BioMed Central Ltd. This article is in the public domain.