Holst H, Ohlsson M, Peterson C, Edenbrandt L
Department of Clinical Physiology, Lund University, Sweden.
Clin Physiol. 1998 Mar;18(2):139-47. doi: 10.1046/j.1365-2281.1998.00087.x.
The purpose of this study was to explore the feasibility of developing artificial neural networks that are able to provide confidence measures for their diagnostic advice. Computer-aided decision making can improve physician performance, but many physicians hesitate to use these 'black boxes'. If we are to rely upon decision support systems for such tasks as medical diagnosis it is essential that the computers indicate when the advice given is based on experience, i.e. give a confidence measure. An artificial neural network was trained to diagnose healed anterior myocardial infarction and to indicate 'lack of experience' when test electrocardiograms were different from the electrocardiograms of the training set. A database of 1249 electrocardiograms from patients who had undergone cardiac catheterization was used to train and test the neural network. Thereafter, the ability of the network to indicate 'lack of experience' was assessed using 100 left bundle branch block electrocardiograms, an electrocardiographic pattern that was excluded from the training set. The network indicated that 83% of the left bundle branch block electrocardiograms and 1% of the test electrocardiograms from catheterized patients were different from the electrocardiograms of the training set. All but one of the left bundle branch block electrocardiograms would otherwise be falsely classified as anterior myocardial infarction by the network. Artificial neural networks can be trained to indicate 'lack of experience', and this ability increases the possibility for neural networks to be accepted as reliable decision support systems in clinical practice.
本研究的目的是探索开发能够为其诊断建议提供置信度度量的人工神经网络的可行性。计算机辅助决策可以提高医生的工作表现，但许多医生对使用这些“黑匣子”犹豫不决。如果我们要依靠决策支持系统来完成诸如医学诊断等任务，那么计算机必须能够指出所给出的建议何时基于经验，即给出一个置信度度量。训练了一个人工神经网络来诊断陈旧性前壁心肌梗死，并在测试心电图与训练集心电图不同时指出“经验不足”。使用来自接受过心脏导管插入术患者的1249份心电图数据库来训练和测试该神经网络。此后,使用100份左束支传导阻滞心电图（一种被排除在训练集之外的心电图模式）评估该网络指出“经验不足”的能力。该网络表明，83%的左束支传导阻滞心电图以及来自接受导管插入术患者的1%的测试心电图与训练集心电图不同。否则，除一份左束支传导阻滞心电图外，所有其他左束支传导阻滞心电图都会被该网络错误地分类为前壁心肌梗死。可以训练人工神经网络来指出“经验不足”，这种能力增加了神经网络在临床实践中被接受为可靠决策支持系统的可能性。
