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Precursor-induced conditional random fields: connecting separate entities by induction for improved clinical named entity recognition

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Authors
Lee, Wangjin; Choi, Jinwook
Issue Date
2019-07-15
Publisher
BioMed Central
Citation
BMC Medical Informatics and Decision Making. 19(1):132
Keywords
Clinical named entity recognitionConditional random fieldsHigh-order dependencyClinical natural language processingInduction method
Abstract
Background
This paper presents a conditional random fields (CRF) method that enables the capture of specific high-order label transition factors to improve clinical named entity recognition performance. Consecutive clinical entities in a sentence are usually separated from each other, and the textual descriptions in clinical narrative documents frequently indicate causal or posterior relationships that can be used to facilitate clinical named entity recognition. However, the CRF that is generally used for named entity recognition is a first-order model that constrains label transition dependency of adjoining labels under the Markov assumption.

Methods
Based on the first-order structure, our proposed model utilizes non-entity tokens between separated entities as an information transmission medium by applying a label induction method. The model is referred to as precursor-induced CRF because its non-entity state memorizes precursor entity information, and the model’s structure allows the precursor entity information to propagate forward through the label sequence.

Results
We compared the proposed model with both first- and second-order CRFs in terms of their F1-scores, using two clinical named entity recognition corpora (the i2b2 2012 challenge and the Seoul National University Hospital electronic health record). The proposed model demonstrated better entity recognition performance than both the first- and second-order CRFs and was also more efficient than the higher-order model.

Conclusion
The proposed precursor-induced CRF which uses non-entity labels as label transition information improves entity recognition F1 score by exploiting long-distance transition factors without exponentially increasing the computational time. In contrast, a conventional second-order CRF model that uses longer distance transition factors showed even worse results than the first-order model and required the longest computation time. Thus, the proposed model could offer a considerable performance improvement over current clinical named entity recognition methods based on the CRF models.
ISSN
1472-6947
Language
English
URI
http://hdl.handle.net/10371/160718
DOI
https://doi.org/10.1186/s12911-019-0865-1
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College of Medicine/School of Medicine (의과대학/대학원)Biomedical Engineering (의공학전공)Journal Papers (저널논문_의공학전공)
College of Engineering/Engineering Practice School (공과대학/대학원)Program in Bioengineering (협동과정-바이오엔지니어링전공)Journal Papers (저널논문_협동과정-바이오엔지니어링전공)
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