Dybowski R., Gant V. (eds.) Clinical Applications of Artificial Neural Networks

Cambridge University Press, 2007. — 380 p.Artificial neural networks provides a powerful tool to help doctors analyze, model, and make sense of complex clinical data across a broad range of medical applications. Their potential in clinical medicine is reflected in the diversity of topics covered in this cutting-edge volume. In addition to looking at new and forthcoming applications the book looks forward to exciting future prospects on the horizon. The volume also examines ethical and legal concerns about the use of "black-box" systems as decision aids in medicine. This eclectic collection of chapters provides an exciting overview of current and future prospects for harnessing the power of artificial neural networks in the investigation and treatment of disease.Applications
Artificial neural networks in laboratory medicine
Using artificial neural networks to screen cervical smears: how new technology enhances health care
Neural network analysis of sleep disorders
Artificial neural networks for neonatal intensive care
Artificial neural networks in urology: applications, feature extraction and user implementations
Artificial neural networks as a tool for whole organism fingerprinting in bacterial taxonomy
Prospects
Recent advances in EEG signal analysis and classification
Adaptive resonance theory: a foundation for ‘apprentice’ systems in clinical decision support?
Evolving artificial neural networks
Theory
Neural networks as statistical methods in survival analysis
A review of techniques for extracting rules from trained artificial neural networks
Confidence intervals and prediction intervals for feedforward neural networks
Ethics and clinical prospects
Artificial neural networks: practical considerations for clinical application