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Mendeley readers
Attention Score in Context
| Title |
A two-step approach for mining patient treatment pathways in administrative healthcare databases
|
|---|---|
| Published in |
Artificial Intelligence in Medicine, April 2018
|
| DOI | 10.1016/j.artmed.2018.03.004 |
| Pubmed ID | |
| Authors |
Ahmed Najjar, Daniel Reinharz, Catherine Girouard, Christian Gagné |
| Abstract |
Clustering electronic medical records allows the discovery of information on healthcare practices. Entries in such medical records are usually composed of a succession of diagnostics or therapeutic steps. The corresponding processes are complex and heterogeneous since they depend on medical knowledge integrating clinical guidelines, the physician's individual experience, and patient data and conditions. To analyze such data, we are first proposing to cluster medical visits, consultations, and hospital stays into homogeneous groups, and then to construct higher-level patient treatment pathways over these different groups. These pathways are then also clustered to distill typical pathways, enabling interpretation of clusters by experts. This approach is evaluated on a real-world administrative database of elderly people in Québec suffering from heart failures. |
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 % |
|---|---|---|
| Canada | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 106 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 23% |
| Student > Master | 21 | 20% |
| Researcher | 11 | 10% |
| Student > Bachelor | 8 | 8% |
| Professor | 4 | 4% |
| Other | 14 | 13% |
| Unknown | 24 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 32 | 30% |
| Engineering | 14 | 13% |
| Medicine and Dentistry | 9 | 8% |
| Mathematics | 3 | 3% |
| Business, Management and Accounting | 3 | 3% |
| Other | 11 | 10% |
| Unknown | 34 | 32% |
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 27 May 2018.
All research outputs
#17,292,294
of 25,382,440 outputs
Outputs from Artificial Intelligence in Medicine
#611
of 914 outputs
Outputs of similar age
#221,432
of 343,332 outputs
Outputs of similar age from Artificial Intelligence in Medicine
#9
of 12 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 914 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 25th percentile – i.e., 25% 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 343,332 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.