Title |
Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
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Published in |
BMC Medical Informatics and Decision Making, April 2013
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DOI | 10.1186/1472-6947-13-s1-s1 |
Pubmed ID | |
Authors |
Buzhou Tang, Hongxin Cao, Yonghui Wu, Min Jiang, Hua Xu |
Abstract |
Named entity recognition (NER) is an important task in clinical natural language processing (NLP) research. Machine learning (ML) based NER methods have shown good performance in recognizing entities in clinical text. Algorithms and features are two important factors that largely affect the performance of ML-based NER systems. Conditional Random Fields (CRFs), a sequential labelling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are two typical machine learning algorithms that have been widely applied to clinical NER tasks. For features, syntactic and semantic information of context words has often been used in clinical NER systems. However, Structural Support Vector Machines (SSVMs), an algorithm that combines the advantages of both CRFs and SVMs, and word representation features, which contain word-level back-off information over large unlabelled corpus by unsupervised algorithms, have not been extensively investigated for clinical text processing. Therefore, the primary goal of this study is to evaluate the use of SSVMs and word representation features in clinical NER tasks. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 4% |
Belgium | 1 | <1% |
Netherlands | 1 | <1% |
Spain | 1 | <1% |
Russia | 1 | <1% |
Unknown | 118 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 26 | 20% |
Student > Master | 21 | 17% |
Researcher | 20 | 16% |
Student > Doctoral Student | 8 | 6% |
Student > Bachelor | 7 | 6% |
Other | 25 | 20% |
Unknown | 20 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 51 | 40% |
Medicine and Dentistry | 19 | 15% |
Agricultural and Biological Sciences | 8 | 6% |
Engineering | 5 | 4% |
Psychology | 5 | 4% |
Other | 14 | 11% |
Unknown | 25 | 20% |