(Q52257271)

28 April 2018

title

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

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28 April 2018

artificial neural network

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1 reference

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R Farouk

5

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author name string

L Bottaci

1

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28 April 2018

P J Drew

2

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28 April 2018

J E Hartley

3

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28 April 2018

M B Hadfield

4

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28 April 2018

P W Lee

6

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28 April 2018

I M Macintyre

7

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28 April 2018

G S Duthie

8

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28 April 2018

J R Monson

9

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28 April 2018

language of work or name

English

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publication date

1 August 1997

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28 April 2018

published in

The Lancet

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28 April 2018

volume

350

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28 April 2018

issue

9076

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28 April 2018

page(s)

469-472

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28 April 2018

Dukes' classification revisited. Findings from the National Surgical Adjuvant Breast and Bowel Projects (Protocol R-01).

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Pathologic determinants of survival associated with colorectal cancer with lymph node metastases. A multivariate analysis of 579 patients

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Learning representations by back-propagating errors

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Diagnostic tests 2: Predictive values

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Use of an artificial neural network for the diagnosis of myocardial infarction

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Application of artificial neural networks to clinical medicine

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Predicting outcomes after liver transplantation. A connectionist approach.

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A practical application of neural network analysis for predicting outcome of individual breast cancer patients

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A demonstration that breast cancer recurrence can be predicted by neural network analysis

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Survival analysis of censored data: neural network analysis detection of complex interactions between variables

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Prognostic factors: rationale and methods of analysis and integration

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A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching

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7 January 2021

Computer-aided Diagnosis: Description of an Adaptable System, and Operational Experience with 2,034 Cases

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MEDICAL DIAGNOSIS: PRESENT, PAST, AND FUTURE. I. PRESENT CONCEPTS OF THE MEANING AND LIMITATIONS OF MEDICAL DIAGNOSIS.

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MEDICAL DIAGNOSIS: PRESENT, PAST, AND FUTURE. III. DIAGNOSIS IN THE FUTURE, INCLUDING A CRITIQUE ON THE USE OF ELECTRONIC COMPUTERS AS DIAGNOSTIC AIDS TO THE PHYSICIAN.

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Mathematical methods in medical diagnosis.

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A technique for using neural network analysis to perform survival analysis of censored data

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Introduction to neural networks.

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Application of the fuzzy ARTMAP neural network model to medical pattern classification tasks.

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Identifiers

DOI

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28 April 2018

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28 April 2018