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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Machine Learning in Orthopedics: A Literature Review
|
|---|---|
| Published in |
Frontiers in Bioengineering and Biotechnology, June 2018
|
| DOI | 10.3389/fbioe.2018.00075 |
| Pubmed ID | |
| Authors |
Federico Cabitza, Angela Locoro, Giuseppe Banfi |
| Abstract |
In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 20% |
| Brazil | 1 | 20% |
| United States | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 296 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 296 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 35 | 12% |
| Student > Ph. D. Student | 31 | 10% |
| Researcher | 27 | 9% |
| Student > Doctoral Student | 24 | 8% |
| Student > Bachelor | 18 | 6% |
| Other | 53 | 18% |
| Unknown | 108 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 47 | 16% |
| Medicine and Dentistry | 44 | 15% |
| Computer Science | 39 | 13% |
| Business, Management and Accounting | 7 | 2% |
| Agricultural and Biological Sciences | 4 | 1% |
| Other | 29 | 10% |
| Unknown | 126 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 August 2022.
All research outputs
#6,246,338
of 24,777,509 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#899
of 8,045 outputs
Outputs of similar age
#100,175
of 334,912 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#21
of 51 outputs
Altmetric has tracked 24,777,509 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 8,045 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 88% 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 334,912 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 51 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.