Outstanding Issues for Clinical Decision Support with Neural Networks

[1]  David J. Spiegelhalter,et al.  Bayesian graphical modelling: a case‐study in monitoring health outcomes , 2002 .

[2]  Paulo J. G. Lisboa,et al.  The role of multiple, linear-projection based visualization techniques in RBF-based classification of high dimensional data , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[3]  Paulo J. G. Lisboa,et al.  Artificial Neural Networks in Biomedicine , 2000, Perspectives in Neural Computing.

[4]  R. F. Wagner,et al.  Classifier design for computer-aided diagnosis: effects of finite sample size on the mean performance of classical and neural network classifiers. , 1999, Medical physics.

[5]  Amparo Alonso-Betanzos,et al.  Applying statistical, uncertainty-based and connectionist approaches to the prediction of fetal outcome: a comparative study , 1999, Artif. Intell. Medicine.

[6]  Mattfeldt,et al.  Prediction of prostatic cancer progression after radical prostatectomy using artificial neural networks: a feasibility study , 1999, BJU international.

[7]  Tom Heskes,et al.  Pruning Using Parameter and Neuronal Metrics , 1999, Neural Computation.

[8]  K. Wildes,et al.  More questions than answers: the commodification of health care. , 1999, The Journal of medicine and philosophy.

[9]  Lucila Ohno-Machado,et al.  Evaluating variable selection methods for diagnosis of myocardial infarction , 1999, AMIA.

[10]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[11]  L. S. S. Wong,et al.  A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks , 1999 .

[12]  Paulo J. G. Lisboa,et al.  Survival of breast cancer patients following surgery: a detailed assessment of the multi-layer perceptron and Cox's proportional hazard model , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[13]  L. Mango,et al.  Neural Network–Assisted Analysis and Microscopic Rescreening in Presumed Negative Cervical Cytologic Smears , 1998, Acta Cytologica.

[14]  G. Khushf.  A radical rupture in the paradigm of modern medicine: conflicts of interest, fiduciary obligations, and the scientific ideal. , 1998, The Journal of medicine and philosophy.

[15]  Christopher M. Bishop,et al.  Regression with Input-Dependent Noise: A Bayesian Treatment , 1996, NIPS.

[16]  C B Schechter,et al.  Cost-effectiveness of rescreening conventionally prepared cervical smears by PAPNET testing. , 1996, Acta cytologica.

[17]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[18]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[19]  Edward H. Shortliffe,et al.  The adolescence of AI in medicine: will the field come of age in the '90s? , 1993, Artif. Intell. Medicine.

[20]  David G. Lowe,et al.  Optimized Feature Extraction and the Bayes Decision in Feed-Forward Classifier Networks , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[22]  J. C. Fisher,et al.  A simple substitution model of technological change , 1971 .