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An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer

Overview of attention for article published in Frontiers in oncology, February 2023
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2 X users

Citations

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6 Dimensions

Readers on

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12 Mendeley
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Title
An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer
Published in
Frontiers in oncology, February 2023
DOI 10.3389/fonc.2023.1124458
Pubmed ID
Authors

Hanlin Wang, Xue Bai, Yajuan Wang, Yanfei Lu, Binbing Wang

X Demographics

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 17%
Researcher 2 17%
Professor 1 8%
Student > Doctoral Student 1 8%
Unknown 6 50%
Readers by discipline Count As %
Computer Science 2 17%
Physics and Astronomy 2 17%
Engineering 2 17%
Unknown 6 50%
Attention Score in Context

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 05 February 2023.
All research outputs
#21,381,945
of 26,246,850 outputs
Outputs from Frontiers in oncology
#11,793
of 22,949 outputs
Outputs of similar age
#362,728
of 487,945 outputs
Outputs of similar age from Frontiers in oncology
#777
of 1,399 outputs
Altmetric has tracked 26,246,850 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,949 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 27th percentile – i.e., 27% 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 487,945 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,399 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.