Literature DB >> 10396251

Artificial intelligence for clinicians.

P J Drew, J R Monson.   

Abstract

Mesh：

Year:  1999        PMID： 10396251      PMCID： PMC1297097          DOI： 10.1177/014107689909200302

Source DB:  PubMed          Journal:  J R Soc Med        ISSN： 0141-0768            Impact factor:   5.344


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  18 in total

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

Authors:  L Bottaci; P J Drew; J E Hartley; M B Hadfield; R Farouk; P W Lee; I M Macintyre; G S Duthie; J R Monson
Journal:  Lancet       Date:  1997-08-16       Impact factor: 79.321

Review 2.  A new biological framework for cancer research.

Authors:  H Schipper; E A Turley; M Baum
Journal:  Lancet       Date:  1996-10-26       Impact factor: 79.321

3.  Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside.

Authors:  A L Goldberger
Journal:  Lancet       Date:  1996-05-11       Impact factor: 79.321

4.  Regression and recursive partition strategies in the analysis of medical survival data.

Authors:  A Ciampi; J F Lawless; S M McKinney; K Singhal
Journal:  J Clin Epidemiol       Date:  1988       Impact factor: 6.437

5.  Introduction to neural networks.

Authors:  S S Cross; R F Harrison; R L Kennedy
Journal:  Lancet       Date:  1995-10-21       Impact factor: 79.321

Review 6.  Application of artificial neural networks to clinical medicine.

Authors:  W G Baxt
Journal:  Lancet       Date:  1995-10-28       Impact factor: 79.321

Review 7.  Prognostic factors: rationale and methods of analysis and integration.

Authors:  G M Clark; S G Hilsenbeck; P M Ravdin; M De Laurentiis; C K Osborne
Journal:  Breast Cancer Res Treat       Date:  1994       Impact factor: 4.872

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

Authors:  M De Laurentiis; P M Ravdin
Journal:  Breast Cancer Res Treat       Date:  1994       Impact factor: 4.872

9.  Artificial neural networks improve the accuracy of cancer survival prediction.

Authors:  H B Burke; P H Goodman; D B Rosen; D E Henson; J N Weinstein; F E Harrell; J R Marks; D P Winchester; D G Bostwick
Journal:  Cancer       Date:  1997-02-15       Impact factor: 6.860

10.  Regression models for prognostic prediction: advantages, problems, and suggested solutions.

Authors:  F E Harrell; K L Lee; D B Matchar; T A Reichert
Journal:  Cancer Treat Rep       Date:  1985-10
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  1 in total

1.  Artificial neural networks.

Authors:  D Partridge; S Rae; W J Wang
Journal:  J R Soc Med       Date:  1999-07       Impact factor: 5.344
  1 in total

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