| Title |
From free text to clusters of content in health records: an unsupervised graph partitioning approach
|
|---|---|
| Published in |
Applied Network Science, January 2019
|
| DOI | 10.1007/s41109-018-0109-9 |
| Pubmed ID | |
| Authors |
M. Tarik Altuncu, Erik Mayer, Sophia N. Yaliraki, Mauricio Barahona |
X Demographics
The data shown below were collected from the profiles of 45 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 | 14 | 31% |
| United States | 5 | 11% |
| France | 1 | 2% |
| Romania | 1 | 2% |
| Canada | 1 | 2% |
| Spain | 1 | 2% |
| Netherlands | 1 | 2% |
| Indonesia | 1 | 2% |
| Unknown | 20 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 26 | 58% |
| Scientists | 17 | 38% |
| Practitioners (doctors, other healthcare professionals) | 1 | 2% |
| Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 51 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 18% |
| Student > Bachelor | 6 | 12% |
| Researcher | 6 | 12% |
| Student > Master | 5 | 10% |
| Student > Doctoral Student | 4 | 8% |
| Other | 6 | 12% |
| Unknown | 15 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 25% |
| Engineering | 4 | 8% |
| Nursing and Health Professions | 3 | 6% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Mathematics | 2 | 4% |
| Other | 11 | 22% |
| Unknown | 16 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 27. 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 03 November 2019.
All research outputs
#1,434,154
of 25,340,976 outputs
Outputs from Applied Network Science
#24
of 581 outputs
Outputs of similar age
#33,504
of 450,781 outputs
Outputs of similar age from Applied Network Science
#1
of 7 outputs
Altmetric has tracked 25,340,976 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 581 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has done particularly well, scoring higher than 96% 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 450,781 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them