Neural network in medical application: A review

Artificial Neural Networks or widely known as Neural networks (ANNs or NNs) is a computational paradigm that comprises of mathematical, statistical, biological sciences and philosophy. These paradigms formulate a formula to form a brain like function, called artificial neuron. Artificial neuron comprises of large number of computational processing elements called units, nodes or cells. Analogously, these processing elements mimic the processing elements of biological neuron. This paper discusses neural network as a powerful tool to enhance current medical prognostic techniques. One of the most popular learning algorithms that are backpropagation algorithm is discussed. Several applications of neural network in medical application are reviewed.

[1]  F. Passold,et al.  Hybrid expert system in anesthesiology for critical patients , 1996, Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96).

[2]  Warren S. Sarle,et al.  Neural Networks and Statistical Models , 1994 .

[3]  Rosaria Silipo,et al.  Artificial neural networks for automatic ECG analysis , 1998, IEEE Trans. Signal Process..

[4]  J. Fricker Artificial neural networks improve diagnosis of acute myocardial infarction , 1997, The Lancet.

[5]  L. Ohno-Machado,et al.  Neural network applications in physical medicine and rehabilitation. , 1999, American journal of physical medicine & rehabilitation.

[6]  Carsten Peterson,et al.  Clustering ECG complexes using Hermite functions and self-organizing maps , 2000, IEEE Trans. Biomed. Eng..

[7]  Mark A. Musen,et al.  Medical applications of artificial neural networks : connectionist models of survival , 1996 .

[8]  Russ B. Altman,et al.  AI in Medicine: The Spectrum of Challenges from Managed Care to Molecular Medicine , 1999, AI Mag..

[9]  S. Walczak,et al.  Use of an artificial neural network to predict length of stay in acute pancreatitis. , 1998, The American surgeon.

[10]  L. Bottaci,et al.  Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions , 1997, The Lancet.

[11]  Mattias Ohlsson,et al.  Acute Myocardial Infarction: Analysis of the ECG Using Artificial Neural Networks , 2000, ANNIMAB.

[12]  G. Dorffner,et al.  Application of artificial neural networks for detection of abnormal fetal heart rate pattern: a comparison with conventional algorithms. , 1999, Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology.

[13]  Riccardo Poli,et al.  Optimum Segmentation of Medical Images with Hopfield Neural Networks , 1995 .

[14]  K. Prank,et al.  Predictive Neural Networks for Learning the Time Course of Blood Glucose Levels from the Complex Interaction of Counterregulatory Hormones , 1998, Neural Computation.

[15]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[16]  Raymond L. Watrous,et al.  A patient-adaptive neural network ECG patient monitoring algorithm , 1995, Computers in Cardiology 1995.

[17]  Richard Dybowski,et al.  Neural Computation in Medicine: Perspectives and Prospects , 2000, ANNIMAB.