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Attention Score in Context
| Title |
Pooling annotated corpora for clinical concept extraction
|
|---|---|
| Published in |
Journal of Biomedical Semantics, January 2013
|
| DOI | 10.1186/2041-1480-4-3 |
| Pubmed ID | |
| Authors |
Kavishwar B Wagholikar, Manabu Torii, Siddhartha R Jonnalagadda, Hongfang Liu |
| Abstract |
The availability of annotated corpora has facilitated the application of machine learning algorithms to concept extraction from clinical notes. However, high expenditure and labor are required for creating the annotations. A potential alternative is to reuse existing corpora from other institutions by pooling with local corpora, for training machine taggers. In this paper we have investigated the latter approach by pooling corpora from 2010 i2b2/VA NLP challenge and Mayo Clinic Rochester, to evaluate taggers for recognition of medical problems. The corpora were annotated for medical problems, but with different guidelines. The taggers were constructed using an existing tagging system MedTagger that consisted of dictionary lookup, part of speech (POS) tagging and machine learning for named entity prediction and concept extraction. We hope that our current work will be a useful case study for facilitating reuse of annotated corpora across institutions. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 7% |
| Netherlands | 1 | 3% |
| Slovenia | 1 | 3% |
| Unknown | 26 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 40% |
| Student > Ph. D. Student | 6 | 20% |
| Other | 3 | 10% |
| Professor > Associate Professor | 3 | 10% |
| Student > Master | 2 | 7% |
| Other | 1 | 3% |
| Unknown | 3 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 47% |
| Linguistics | 3 | 10% |
| Agricultural and Biological Sciences | 3 | 10% |
| Physics and Astronomy | 2 | 7% |
| Medicine and Dentistry | 2 | 7% |
| Other | 3 | 10% |
| Unknown | 3 | 10% |
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 09 January 2013.
All research outputs
#14,159,409
of 22,691,736 outputs
Outputs from Journal of Biomedical Semantics
#213
of 364 outputs
Outputs of similar age
#168,361
of 282,035 outputs
Outputs of similar age from Journal of Biomedical Semantics
#21
of 32 outputs
Altmetric has tracked 22,691,736 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 39th percentile – i.e., 39% 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 282,035 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.