Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance
Ali Alim-Marvasti, Fernando Pérez-García, Karan Dahele, Gloria Romagnoli, Beate Diehl, Rachel Sparks, Sebastien Ourselin, Matthew J. Clarkson, John S. Duncan
This research output has an Altmetric Attention Score of 3. 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 04 November 2021.
All research outputs
#14,463,631
of 25,159,758 outputs
Outputs from Frontiers in Digital Health
#366
of 787 outputs
Outputs of similar age
#246,744
of 528,242 outputs
Outputs of similar age from Frontiers in Digital Health
#20
of 40 outputs
Altmetric has tracked 25,159,758 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 787 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has gotten more attention than average, scoring higher than 51% 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 528,242 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 52% of its contemporaries.
We're also able to compare this research output to 40 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 50% of its contemporaries.