Neural Networks in Clinical Medicine

Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical decision-making applications. It includes detailed discussion of the issues particularly relevant to medical data as well as wider issues relevant to any neural net application. The article is restricted to back-propagation learning in multilayer perceptrons, as this is the neural net model most widely used in medical applications. Key words: neural networks; medical decision making; pattern recognition; nonlinearity; error back-propagation; multi layer perceptron. (Med Decis Making 1996;16:386-398)

[1]  Jerome H. Friedman,et al.  A Recursive Partitioning Decision Rule for Nonparametric Classification , 1977, IEEE Transactions on Computers.

[2]  D. Kleinbaum,et al.  Applied Regression Analysis and Other Multivariate Methods , 1978 .

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

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  Peter Jackson,et al.  Introduction to expert systems , 1986 .

[6]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[7]  J. Fleiss,et al.  Quantification of agreement in psychiatric diagnosis revisited. , 1987, Archives of general psychiatry.

[8]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[9]  Terrence J. Sejnowski,et al.  Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..

[10]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[11]  Gerald Tesauro,et al.  Scaling Relationships in Back-propagation Learning , 1988, Complex Syst..

[12]  Emile Servan-Schreiber,et al.  A Connectionist Approach to the Diagnosis of Dementia , 1988 .

[13]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[14]  R. Lippmann Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[15]  Hervé Bourlard,et al.  Generalization and Parameter Estimation in Feedforward Netws: Some Experiments , 1989, NIPS.

[16]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[17]  Igor Aleksander,et al.  Introduction to Neural Computing , 1990 .

[18]  A. Marmarou,et al.  Classification of somatosensory-evoked potentials recorded from patients with severe head injuries , 1990, IEEE Engineering in Medicine and Biology Magazine.

[19]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[20]  M L Meistrell,et al.  Evaluation of neural network performance by receiver operating characteristic (ROC) analysis: examples from the biotechnology domain. , 1989, Computer methods and programs in biomedicine.

[21]  A Hart,et al.  Evaluating black-boxes as medical decision aids: issues arising from a study of neural networks. , 1990, Medical informatics = Medecine et informatique.

[22]  Dan Jones Neural networks for medical diagnosis , 1990 .

[23]  D. Lowe,et al.  Exploiting prior knowledge in network optimization: an illustration from medical prognosis , 1990 .

[24]  Derek F. Stubbs Multiple neural network approaches to clinical expert systems , 1990, Defense, Security, and Sensing.

[25]  David G. Bounds,et al.  A comparison of neural network and other pattern recognition approaches to the diagnosis of low back disorders , 1990, Neural Networks.

[26]  W. Baxt Use of an artificial neural network for the diagnosis of myocardial infarction. , 1991, Annals of internal medicine.

[27]  G Reibnegger,et al.  Neural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[28]  A. Detsky,et al.  Neural networks: what are they? , 1991, Annals of internal medicine.

[29]  J. Utans,et al.  Selecting neural network architectures via the prediction risk: application to corporate bond rating prediction , 1991, Proceedings First International Conference on Artificial Intelligence Applications on Wall Street.

[30]  Richard Lippmann,et al.  Neural Network Classifiers Estimate Bayesian a posteriori Probabilities , 1991, Neural Computation.

[31]  D. Machin,et al.  Medical Statistics: A Commonsense Approach , 1993 .

[32]  Robert F. Harrison,et al.  Neural classification of chest pain symptoms: a comparative study , 1991 .

[33]  E. Somoza,et al.  Comparing and Optimizing Diagnostic Tests , 1992, Medical decision making : an international journal of the Society for Medical Decision Making.

[34]  P. Wilding,et al.  The application of backpropagation neural networks to problems in pathology and laboratory medicine. , 1992, Archives of pathology & laboratory medicine.

[35]  Gerald Tesauro,et al.  How Tight Are the Vapnik-Chervonenkis Bounds? , 1992, Neural Computation.

[36]  K A Spackman Combining logistic regression and neural networks to create predictive models. , 1992, Proceedings. Symposium on Computer Applications in Medical Care.

[37]  P. Wilding,et al.  Application of neural networks to the interpretation of laboratory data in cancer diagnosis. , 1992, Clinical chemistry.

[38]  W. Baxt Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction. , 1992, Annals of emergency medicine.

[39]  I. Modai,et al.  Clinical Decisions for Psychiatric Inpatients and Their Evaluation by a Trained Neural Network , 1993, Methods of Information in Medicine.

[40]  J. Tu,et al.  Use of a neural network as a predictive instrument for length of stay in the intensive care unit following cardiac surgery. , 1993, Computers and biomedical research, an international journal.

[41]  M. Rubenfire,et al.  Neural network in the clinical diagnosis of acute pulmonary embolism. , 1993, Chest.

[42]  E. Somoza,et al.  A Neural-network Approach to Predicting Admission Decisions in a Psychiatric Emergency Room , 1993, Medical decision making : an international journal of the Society for Medical Decision Making.

[43]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[44]  Ferdinand Hergert,et al.  Improving model selection by nonconvergent methods , 1993, Neural Networks.

[45]  M L Astion,et al.  Overtraining in neural networks that interpret clinical data. , 1993, Clinical chemistry.

[46]  J. Sugar,et al.  Application of multivariate, fuzzy set and neural network analysis in quantitative cytological examinations. , 1993, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[47]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[48]  Martin Brown,et al.  Intelligent Control - Aspects of Fuzzy Logic and Neural Nets , 1993, World Scientific Series in Robotics and Intelligent Systems.

[49]  B. Kosko Fuzzy Thinking: The New Science of Fuzzy Logic , 1993 .

[50]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[51]  Mann A. Shoffner,et al.  Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. , 1994, Cancer letters.

[52]  Z. Shen An application of neural networks for the detection of coronary heart disease. , 1994 .

[53]  S K Rogers,et al.  Artificial neural networks for early detection and diagnosis of cancer. , 1994, Cancer letters.

[54]  M De Laurentiis,et al.  A technique for using neural network analysis to perform survival analysis of censored data. , 1994, Cancer letters.

[55]  Halbert White,et al.  Bootstrapping Confidence Intervals for Clinical Input Variable Effects in a Network Trained to Identify the Presence of Acute Myocardial Infarction , 1995, Neural Computation.

[56]  M. Cohen,et al.  Comparative Approaches to Medical Reasoning , 1995 .

[57]  Kenneth W. Bauer,et al.  Determining input features for multilayer perceptrons , 1995, Neurocomputing.

[58]  Robert Tibshirani,et al.  A Comparison of Some Error Estimates for Neural Network Models , 1996, Neural Computation.

[59]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .