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Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods

Overview of attention for article published in BMC Cancer, December 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
3 tweeters
facebook
1 Facebook page

Citations

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7 Dimensions

Readers on

mendeley
36 Mendeley
Title
Prediction of 5–year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods
Published in
BMC Cancer, December 2017
DOI 10.1186/s12885-017-3806-3
Pubmed ID
Authors

Bogdan Obrzut, Maciej Kusy, Andrzej Semczuk, Marzanna Obrzut, Jacek Kluska

Abstract

Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5-year overall survival prediction in patients with cervical cancer treated by radical hysterectomy. The data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model. The best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse. The PNN model is an effective tool for predicting 5-year overall survival in cervical cancer patients treated with radical hysterectomy.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 11 31%
Student > Bachelor 7 19%
Student > Ph. D. Student 4 11%
Researcher 3 8%
Student > Doctoral Student 3 8%
Other 8 22%
Readers by discipline Count As %
Unspecified 13 36%
Medicine and Dentistry 11 31%
Computer Science 6 17%
Psychology 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 4 11%

Attention Score in Context

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 06 February 2018.
All research outputs
#6,447,210
of 12,470,921 outputs
Outputs from BMC Cancer
#1,375
of 4,616 outputs
Outputs of similar age
#142,114
of 375,079 outputs
Outputs of similar age from BMC Cancer
#132
of 487 outputs
Altmetric has tracked 12,470,921 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,616 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 69% 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 375,079 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 61% of its contemporaries.
We're also able to compare this research output to 487 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 72% of its contemporaries.