Artificial neural networks and survival prediction in ovarian carcinoma: preliminary results

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Artificial neural networks and survival prediction in ovarian carcinoma: preliminary results. / Kehoe, Sean; Lowe, D; Powell, Judith; Vincente, B.

In: European Journal of Gynaecological Oncology, Vol. 21, No. 6, 2000, p. 583-584.

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@article{023425a4d0894919aa3530720bf81996,
title = "Artificial neural networks and survival prediction in ovarian carcinoma: preliminary results",
abstract = "The standard use of known survival predictors for ovarian cancer in clinical practice are primarily based on disease stage. This does not permit a real individualization of a patient's potential outcome. This study assessed the value of neural networks to refine the prediction of survival based only on information gleaned at primary surgery. The possibility exists that such methods may permit further elucidation of outcome and influence management.",
keywords = "artificial neural networks, survival prediction, ovarian carcinoma, preliminary results",
author = "Sean Kehoe and D Lowe and Judith Powell and B Vincente",
year = "2000",
language = "English",
volume = "21",
pages = "583--584",
journal = "European Journal of Gynaecological Oncology",
issn = "0392-2936",
publisher = "S.O.G. CANADA Inc.",
number = "6",

}

RIS

TY - JOUR

T1 - Artificial neural networks and survival prediction in ovarian carcinoma: preliminary results

AU - Kehoe, Sean

AU - Lowe, D

AU - Powell, Judith

AU - Vincente, B

PY - 2000

Y1 - 2000

N2 - The standard use of known survival predictors for ovarian cancer in clinical practice are primarily based on disease stage. This does not permit a real individualization of a patient's potential outcome. This study assessed the value of neural networks to refine the prediction of survival based only on information gleaned at primary surgery. The possibility exists that such methods may permit further elucidation of outcome and influence management.

AB - The standard use of known survival predictors for ovarian cancer in clinical practice are primarily based on disease stage. This does not permit a real individualization of a patient's potential outcome. This study assessed the value of neural networks to refine the prediction of survival based only on information gleaned at primary surgery. The possibility exists that such methods may permit further elucidation of outcome and influence management.

KW - artificial neural networks

KW - survival prediction

KW - ovarian carcinoma

KW - preliminary results

M3 - Article

VL - 21

SP - 583

EP - 584

JO - European Journal of Gynaecological Oncology

JF - European Journal of Gynaecological Oncology

SN - 0392-2936

IS - 6

ER -