Leon M A, Keller J
Medical Informatics-ITS, University of Missouri-Columbia 65211, USA.
Proc AMIA Annu Fall Symp. 1997:183-7.
Artificial neural networks are established analytical methods in bio-medical research. They have repeatedly outperformed traditional tools for pattern recognition and clinical outcome prediction while assuring continued adaptation and learning. However, successful experimental neural networks systems seldom reach a production state. That is, they are not incorporated into clinical information systems. It could be speculated that neural networks simply must undergo a lengthy acceptance process before they become part of the day to day operations of health care systems. However, our experience trying to incorporate experimental neural networks into information systems lead us to believe that there are technical and operational barriers that greatly difficult neural network implementation. A solution for these problems may be the delineation of policies and procedures for neural network implementation and the development a new class of neural network client/server applications that fit the needs of current clinical information systems.
人工神经网络是生物医学研究中已确立的分析方法。在确保持续适应和学习的同时，它们在模式识别和临床结果预测方面的表现 repeatedly outperformed 传统工具。然而，成功的实验性神经网络系统很少进入生产状态。也就是说，它们没有被纳入临床信息系统。可以推测，神经网络在成为医疗保健系统日常运营的一部分之前， simply must undergo 一个漫长的接受过程。然而，我们将实验性神经网络纳入信息系统的尝试经验使我们相信，存在极大地 difficult neural network implementation 的技术和操作障碍。解决这些问题的一个办法可能是划定神经网络实施的政策和程序，并开发一类适合当前临床信息系统需求的新型神经网络客户端/服务器应用程序。
