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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Modelling of inquiry diagnosis for coronary heart disease in traditional Chinese medicine by using multi-label learning
|
|---|---|
| Published in |
BMC Complementary Medicine and Therapies, July 2010
|
| DOI | 10.1186/1472-6882-10-37 |
| Pubmed ID | |
| Authors |
Guo-Ping Liu, Guo-Zheng Li, Ya-Lei Wang, Yi-Qin Wang |
| Abstract |
Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 6 | 16% |
| Student > Postgraduate | 5 | 13% |
| Student > Ph. D. Student | 3 | 8% |
| Student > Master | 3 | 8% |
| Student > Doctoral Student | 2 | 5% |
| Other | 8 | 21% |
| Unknown | 11 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 26% |
| Computer Science | 9 | 24% |
| Engineering | 3 | 8% |
| Mathematics | 2 | 5% |
| Linguistics | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 11 | 29% |
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 08 May 2013.
All research outputs
#17,687,671
of 22,709,015 outputs
Outputs from BMC Complementary Medicine and Therapies
#2,333
of 3,619 outputs
Outputs of similar age
#84,160
of 94,488 outputs
Outputs of similar age from BMC Complementary Medicine and Therapies
#18
of 19 outputs
Altmetric has tracked 22,709,015 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 3,619 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one is in the 31st percentile – i.e., 31% 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 94,488 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.