↓ Skip to main content

Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2013
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
67 Dimensions

Readers on

mendeley
119 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
Published in
BMC Medical Informatics and Decision Making, April 2013
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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Belgium 1 <1%
Netherlands 1 <1%
Spain 1 <1%
Russia 1 <1%
Unknown 110 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 22%
Researcher 20 17%
Student > Master 20 17%
Student > Doctoral Student 9 8%
Student > Bachelor 7 6%
Other 22 18%
Unknown 15 13%
Readers by discipline Count As %
Computer Science 50 42%
Medicine and Dentistry 19 16%
Agricultural and Biological Sciences 9 8%
Engineering 5 4%
Psychology 5 4%
Other 11 9%
Unknown 20 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 April 2020.
All research outputs
#10,473,785
of 17,499,602 outputs
Outputs from BMC Medical Informatics and Decision Making
#932
of 1,585 outputs
Outputs of similar age
#84,063
of 161,940 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 17,499,602 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 161,940 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them