Ohno-Machado L
Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Proc AMIA Annu Fall Symp. 1996:170-4.
Prognostic assessment of patients is a key part of medical care. Although neural networks can be used to model survival, their accuracy has been limited for a variety of factors, including (1) the lack of data balance in certain intervals and (2) the lack of representation of temporal dependencies in the network architecture. Both problems can be solved with the use of sequential neural networks, which establish predictions for a certain time point and then use these predictions to produce survival estimates for other time points. If the sequence of models is adequate, sequential neural networks produce more accurate estimates of survival than standard neural networks, as shown in this example in the domain of AIDS. Assessments of survival in one, two, three, five and six years become more accurate (as measured by the areas under the ROC curves) when initial predictions of survival in four years are used in a sequential neural network model.
患者的预后评估是医疗护理的关键部分。虽然神经网络可用于对生存情况进行建模，但由于多种因素，其准确性受到限制，这些因素包括：（1）某些时间段内数据缺乏平衡；（2）网络架构中缺乏对时间依赖性的表示。使用序列神经网络可以解决这两个问题，序列神经网络会为某个时间点建立预测，然后利用这些预测来生成其他时间点的生存估计值。如果模型序列足够充分，序列神经网络会比标准神经网络生成更准确的生存估计值，如艾滋病领域的这个例子所示。当在序列神经网络模型中使用四年生存情况的初始预测时，对一年、两年、三年、五年和六年生存情况的评估会变得更加准确（通过ROC曲线下面积来衡量）。
