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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, April 2019
|
| DOI | 10.1186/s12911-019-0762-7 |
| Pubmed ID | |
| Authors |
Xiaoling Cai, Shoubin Dong, Jinlong Hu |
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 % |
|---|---|---|
| Nigeria | 1 | 50% |
| France | 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 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 71 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 11% |
| Student > Ph. D. Student | 7 | 10% |
| Other | 4 | 6% |
| Lecturer | 4 | 6% |
| Student > Bachelor | 3 | 4% |
| Other | 15 | 21% |
| Unknown | 30 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 20 | 28% |
| Medicine and Dentistry | 4 | 6% |
| Nursing and Health Professions | 2 | 3% |
| Neuroscience | 2 | 3% |
| Agricultural and Biological Sciences | 1 | 1% |
| Other | 8 | 11% |
| Unknown | 34 | 48% |
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 15 January 2020.
All research outputs
#18,937,691
of 24,133,587 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,556
of 2,061 outputs
Outputs of similar age
#255,290
of 356,869 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#29
of 45 outputs
Altmetric has tracked 24,133,587 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,061 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 21st percentile – i.e., 21% 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 356,869 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.