Baxt W G
Department of Emergency Medicine and Medicine, University of California, San Diego Medical Center 92103-8676.
Cancer Lett. 1994 Mar 15;77(2-3):85-93. doi: 10.1016/0304-3835(94)90090-6.
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.
背景表明许多生物过程是混沌的。众所周知，混沌过程可以通过非线性技术进行精确表征。有证据表明，人工神经网络作为一种应用非线性统计的已知方法，在识别有无心肌梗死的患者方面比医生或其他计算机范式表现得更准确。有人认为，性能的提高可能是由于该网络在表征嵌入到该实体临床诊断问题中的混沌过程方面具有更强的能力。
