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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records
|
|---|---|
| Published in |
BMC Bioinformatics, December 2018
|
| DOI | 10.1186/s12859-018-2467-9 |
| Pubmed ID | |
| Authors |
Shanta Chowdhury, Xishuang Dong, Lijun Qian, Xiangfang Li, Yi Guan, Jinfeng Yang, Qiubin Yu |
X Demographics
The data shown below were collected from the profiles of 3 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 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 56 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 16% |
| Researcher | 6 | 11% |
| Student > Master | 5 | 9% |
| Student > Doctoral Student | 4 | 7% |
| Lecturer | 3 | 5% |
| Other | 7 | 13% |
| Unknown | 22 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 25% |
| Engineering | 5 | 9% |
| Medicine and Dentistry | 4 | 7% |
| Agricultural and Biological Sciences | 2 | 4% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 24 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 30 December 2018.
All research outputs
#18,002,039
of 23,120,280 outputs
Outputs from BMC Bioinformatics
#5,984
of 7,330 outputs
Outputs of similar age
#304,826
of 437,310 outputs
Outputs of similar age from BMC Bioinformatics
#161
of 213 outputs
Altmetric has tracked 23,120,280 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,330 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 437,310 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 213 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.