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Mendeley readers
Attention Score in Context
| Title |
Injury narrative text classification using factorization model
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2015
|
| DOI | 10.1186/1472-6947-15-s1-s5 |
| Pubmed ID | |
| Authors |
Lin Chen, Kirsten Vallmuur, Richi Nayak |
| Abstract |
Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93. |
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 % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 8 | 22% |
| Student > Ph. D. Student | 5 | 14% |
| Researcher | 5 | 14% |
| Student > Master | 5 | 14% |
| Other | 3 | 8% |
| Other | 6 | 17% |
| Unknown | 4 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 25% |
| Engineering | 7 | 19% |
| Computer Science | 5 | 14% |
| Psychology | 2 | 6% |
| Business, Management and Accounting | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 8 | 22% |
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 22 May 2015.
All research outputs
#18,410,971
of 22,805,349 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,570
of 1,988 outputs
Outputs of similar age
#192,789
of 266,611 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#37
of 43 outputs
Altmetric has tracked 22,805,349 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,988 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% 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 266,611 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.