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Title |
Mining FDA drug labels using an unsupervised learning technique - topic modeling
|
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Published in |
BMC Bioinformatics, October 2011
|
DOI | 10.1186/1471-2105-12-s10-s11 |
Pubmed ID | |
Authors |
Halil Bisgin, Zhichao Liu, Hong Fang, Xiaowei Xu, Weida Tong |
Abstract |
The Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 3% |
Spain | 1 | <1% |
Malaysia | 1 | <1% |
Unknown | 132 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 26 | 19% |
Researcher | 26 | 19% |
Student > Master | 16 | 12% |
Student > Doctoral Student | 10 | 7% |
Other | 8 | 6% |
Other | 33 | 24% |
Unknown | 19 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 38 | 28% |
Medicine and Dentistry | 15 | 11% |
Agricultural and Biological Sciences | 10 | 7% |
Engineering | 9 | 7% |
Social Sciences | 7 | 5% |
Other | 37 | 27% |
Unknown | 22 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 19 July 2022.
All research outputs
#5,614,802
of 26,560,265 outputs
Outputs from BMC Bioinformatics
#1,877
of 7,820 outputs
Outputs of similar age
#29,876
of 154,265 outputs
Outputs of similar age from BMC Bioinformatics
#25
of 102 outputs
Altmetric has tracked 26,560,265 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,820 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done well, scoring higher than 75% of its peers.
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 154,265 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.