PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Neural networks in survival time prediction

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper the results obtained by a neural network application to predict the survival time in ovarian cancer patients are presented. The neural training was performed using clinical input data from ovarian cancer patients. The results were quite encourageable, with a comparable rate of prediction with standard regression models.
Rocznik
Strony
53--60
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
  • Chemotherapy Dept. of The Medical University in Łódź
  • Chemotherapy Dept. of The Medical University in Łódź
autor
  • The College of Computer Science in Łódź
Bibliografia
  • [1] Hamamoto et al., Prediction of the early prognosis of the hepatectomized patient with hepatocellular carcinoma with a neural network, Comput Biol Med 1995 Jan; 25(l):49-59.
  • [2] Moul J.W. et al., Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer, J Urol 1995 May; 153(5): 1674-7.
  • [3] Faraggi D. et al., A neural network model for survival data, Stat Med 1995 Jan; 14(l):73-82.
  • [4] Ortiz J. et al., One-year mortality prognosis in heart failure: a neural network approach based on echocardiographic data, J Am Coll Cardiol 1995 Dec; 26(7): 1586-93.
  • [5] Christy P.S. et al., Use of a neural network and a multiple regression model to predict histologic grade of astrocytoma from MRI appeaances, Neuroradiology 1995 Feb; 37(2):89-93.
  • [6] Horne C.H. et al., Prediction of nodal metastasis and prognosis in breast cancer: a neural model. Anticancer Res 1997 Jul; 17(4A):2735-41.
  • [7] Naguib R. N. et al., The detection of nodal metastasis in breast cancer using neural network techniques, Physio. Meas 1996 Nov; 17(4):297-303.
  • [8] Ohno Machado L., A comparison of Cox proportional hazards and artificial neural network models for medical prognosis, Comput Biol Med 1997 Jan; 27(l):55-65.
  • [9] El Deredy W., Pretreatment prediction of the chemotherapeutic response of human glioma cell cultures using nuclear magnetic resonance spectroscopy and artificial neural networks, Cancer Res 1997 Oct; 57(19):4196-9.
  • [10] Jefferson M. F. et al., Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma, Cancer 1997 Apr; 79(7): 1338-42.
  • [11] Lo J. Y. et al., Predicting breast cancer invasion with artificial neural networks on the basis of mammographie features, Radiology 1997 Apr; 203(1): 159-63.
  • [12] Oczkowski W. J. et al., Neural network modeling accurately predicts the functional outcome of stroke survivors with moderate disabilities, Arch Phys Med Rehabil 1997 Apr; 78(4):340-5.
  • [13] Bryce T. J. et al., Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck, Int J Radiat Oncol Biol Phys 1998 May; 41(2):339-45.
  • [14] Lundin J., Artificial neural networks in outcome prediction, Ann Chir Gynaecol 1998; 87(2): 128-30.
  • [15] Lo J. Y. et al., Effect of patient history data on the prediction of breast cancer from mammographie findings with artificial neural networks, Acad Radiol 1999 Jan; 6( 1): 10-5.
  • [16] Sierra B. et al., Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches, Artif Intell Med 1998 Sep; 14(l-2):215-30.
  • [17] Burke H. B. et al., Predicting response to adjuvant and radiation therapy in patients with early stage breast carcinoma, Cancer 1998 Mar; 82(5):874-7.
  • [18] Cooper G. F. et al., An evaluation of machine-learning methods for predicting pneumonia mortality, Artif Intell Med 1997 Feb; 9(2): 107-38.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD7-0028-0030
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.