↓ Skip to main content

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2017
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
37 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention
Published in
BMC Medical Informatics and Decision Making, August 2017
DOI 10.1186/s12911-017-0508-3
Pubmed ID
Authors

Omneya Attallah, Alan Karthikesalingam, Peter J. E. Holt, Matthew M. Thompson, Rob Sayers, Matthew J. Bown, Eddie C. Choke, Xianghong Ma

Abstract

Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 22%
Student > Ph. D. Student 7 19%
Student > Master 4 11%
Student > Doctoral Student 3 8%
Researcher 2 5%
Other 3 8%
Unknown 10 27%
Readers by discipline Count As %
Medicine and Dentistry 9 24%
Engineering 5 14%
Computer Science 4 11%
Nursing and Health Professions 1 3%
Agricultural and Biological Sciences 1 3%
Other 6 16%
Unknown 11 30%

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 22 September 2017.
All research outputs
#10,467,678
of 11,809,688 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,046
of 1,073 outputs
Outputs of similar age
#225,794
of 267,431 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#25
of 26 outputs
Altmetric has tracked 11,809,688 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,073 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% 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 267,431 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.