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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs
|
|---|---|
| Published in |
Frontiers in oncology, April 2018
|
| DOI | 10.3389/fonc.2018.00110 |
| Pubmed ID | |
| Authors |
Mary Feng, Gilmer Valdes, Nayha Dixit, Timothy D. Solberg |
| Abstract |
Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 197 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 197 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 35 | 18% |
| Student > Ph. D. Student | 31 | 16% |
| Student > Master | 25 | 13% |
| Student > Bachelor | 13 | 7% |
| Professor > Associate Professor | 11 | 6% |
| Other | 35 | 18% |
| Unknown | 47 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 39 | 20% |
| Physics and Astronomy | 39 | 20% |
| Computer Science | 17 | 9% |
| Engineering | 16 | 8% |
| Nursing and Health Professions | 6 | 3% |
| Other | 22 | 11% |
| Unknown | 58 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 13 January 2022.
All research outputs
#5,242,603
of 25,382,440 outputs
Outputs from Frontiers in oncology
#1,805
of 22,428 outputs
Outputs of similar age
#94,117
of 340,618 outputs
Outputs of similar age from Frontiers in oncology
#32
of 143 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 22,428 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done particularly well, scoring higher than 91% 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 340,618 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 72% of its contemporaries.
We're also able to compare this research output to 143 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.