title: Complexity, chaos and human physiology: the justification for non-linear neural computational analysis.
abstract: Background is presented to suggest that a great many biologic processes are chaotic. It is well known that chaotic processes can be accurately characterized by non-linear technologies. Evidence is presented that an artificial neural network, which is a known method for the application of non-linear statistics, is able to perform more accurately in identifying patients with and without myocardial infarction than either physicians or other computer paradigms. It is suggested that the improved performance may be due to the network's better ability to characterize what is a chaotic process imbedded in the problem of the clinical diagnosis of this entity.
authors.affiliation: Department of Emergency Medicine and Medicine, University of California, San Diego Medical Center 92103-8676.
authors.forename: W G
authors.initials: WG
authors.lastname: Baxt
mesh_terms: D003936:Diagnosis, Computer-Assisted; D004630:Emergencies; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000503:physiopathology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010827:Physiology; D012680:Sensitivity and Specificity