Which is a more accurate predictor in colorectal survival analysis? Nine data mining algorithms vs. the TNM staging system (Q34359394)

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:22848691 AND SRC:MED&resulttype=core&format=json

9 February 2020

title

Which is a more accurate predictor in colorectal survival analysis? Nine data mining algorithms vs. the TNM staging system (English)

1 reference

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9 February 2020

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3

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9 February 2020

author name string

Peng Gao

1

1 reference

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9 February 2020

Xin Zhou

2

1 reference

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9 February 2020

Yong-xi Song

4

1 reference

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9 February 2020

Lin-lin Tong

5

1 reference

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9 February 2020

Ying-ying Xu

6

1 reference

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9 February 2020

Zhen-yu Yue

7

1 reference

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9 February 2020

Hui-mian Xu

8

1 reference

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9 February 2020

language of work or name

English

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publication date

25 July 2012

1 reference

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9 February 2020

1 reference

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:22848691 AND SRC:MED&resulttype=core&format=json

9 February 2020

volume

7

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9 February 2020

issue

7

1 reference

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9 February 2020

page(s)

e42015

1 reference

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Creative Commons Attribution 4.0 International

9 February 2020

copyright license

start time

25 July 2012

1 reference

April 2022 Public Data File from Crossref

copyright status

copyrighted

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describes a project that uses

Bayes' theorem

1 reference

18 June 2022

https://europepmc.org/article/PMC/3404978

based on heuristic

inferred from PubMed Central ID database lookup

cites work

Prediction of foot clearance parameters as a precursor to forecasting the risk of tripping and falling.

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Comparison of the predictive qualities of three prognostic models of colorectal cancer

6 July 2018

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Applications of machine learning in cancer prediction and prognosis

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A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes

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Predicting breast cancer survivability: a comparison of three data mining methods

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Neural networks in the prediction of survival in patients with colorectal cancer

6 July 2018

1 reference

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Neural network and regression predictions of 5-year survival after colon carcinoma treatment

6 July 2018

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Using Bayesian networks to analyze expression data

6 July 2018

1 reference

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Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions.

6 July 2018

1 reference

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A method of comparing the areas under receiver operating characteristic curves derived from the same cases

6 July 2018

1 reference

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Artificial neural networks improve the accuracy of cancer survival prediction.

6 July 2018

1 reference

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Prognostic groups in colorectal carcinoma patients based on tumor cell proliferation and classification and regression tree (CART) survival analysis

21 January 2018

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Is the seventh edition of the UICC/AJCC TNM staging system reasonable for patients with tumor deposits in colorectal cancer?

21 January 2018

1 reference

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Comparing the predictive value of neural network models to logistic regression models on the risk of death for small-cell lung cancer patients

21 January 2018

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Validation of the seventh edition of the American Joint Committee on Cancer tumor-node-metastasis (AJCC TNM) staging in patients with stage II and stage III colorectal carcinoma: analysis of 2511 cases from a medical centre in Korea

21 January 2018

1 reference

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Comparison of fuzzy inference, logistic regression, and classification trees (CART). Prediction of cervical lymph node metastasis in carcinoma of the tongue.

27 October 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=3404978

Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

27 October 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=3404978

Current TNM staging of colorectal cancer

27 October 2018

1 reference

PubMed

https://pubmed.ncbi.nlm.nih.gov/22848691

inferred from PubMed ID database lookup

12 December 2020

based on heuristic

Identifiers

DOI

10.1371/JOURNAL.PONE.0042015

1 reference

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:22848691 AND SRC:MED&resulttype=core&format=json

9 February 2020

0 references

release_tuv4g3hn7zbmbpho2os4vuwzby

1 reference

Fatcat

https://api.fatcat.wiki/v0/release/tuv4g3hn7zbmbpho2os4vuwzby

mapped directly with Wikidata item

24 November 2022

based on heuristic

1 reference

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:22848691 AND SRC:MED&resulttype=core&format=json

9 February 2020

PubMed publication ID

22848691

1 reference

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:22848691 AND SRC:MED&resulttype=core&format=json