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Bootstrapping Clinical Concept Extraction with Self-Training

http://hdl.handle.net/20.500.12678/0000004650
3d55e218-d78d-462b-b804-14e590cff15e
d4bb3896-3314-4b25-9eb6-2c5575b43c95
Publication type
Article
Upload type
Publication
Title
Title Bootstrapping Clinical Concept Extraction with Self-Training
Language en
Publication date 2018-02-22
Authors
Khin, Nyein Pyae Pyae
Lynn, Khin Thidar
Description
In the clinical domain, annotated clinical records are not only expensive but also often unavailable for research due to patient privacy and confidentiality requirements. The challenge is how to train effective clinical concept extraction system especially with small amount of training data. To address the limited supervision problem of insufficient labeled training examples, self-training style semi-supervised bootstrapping approach to concept extraction system is proposed. In self-training a classifier is trained from an initially small amount of human annotated data, and then used to label unlabeled data. The machine-labeled data is then added to the original data set, and the classifier is retrained iteratively. For labeling clinical concepts, Conditional Random Fields (CRF) is chosen due to its promising performance in many sequence labeling tasks.
Keywords
self-training, CRFs, semi-supervised learning, clinical concept extraction
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/297
Journal articles
Sixteenth International Conferences on Computer Applications(ICCA 2018)
Conference papers
Books/reports/chapters
Thesis/dissertations
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