Linder R, König I R, Weimar C, Diener H C, Pöppl S J, Ziegler A
Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany.
Methods Inf Med. 2006;45(5):536-40.
Accurately predicting disease progress from a set of predictive variables is an important aspect of clinical work. For binary outcomes, the classical approach is to develop prognostic logistic regression (LR) models. Alternatively, machine learning algorithms were proposed with artificial neural networks (ANN) having become popular over the last decades. Although some studies have compared predictive accuracies of LR and ANN models, some concerns regarding their methodological quality have been voiced. Our comparison has the advantage of being based on two large independent data sets allowing for elaborate model development and independent validation.
From the German Stroke Database, a learning data set including 1754 prospectively recruited patients with acute ischemic stroke was used. Utilizing LR and ANN, two prognostic models were developed predicting restitution of functional independence and survival after 100 days. The resulting models were applied to classify 1470 patients with acute ischemic stroke; this test data set was collected independently from the learning data. Error fractions in the test data were determined, and differences in error fractions between the algorithms were calculated with 95% confidence intervals.
For most prognostic models, error fractions in the test data were below 40%. There was no difference between the algorithms except for the model predicting completely versus incompletely restituted or deceased patients (difference in error fractions = 4.01% [2.10-5.96%], p = 0.0001).
The conscientiously applied LR remains the gold standard for prognostic modelling; however, ANN can be an alternative automated "quick and easy" multivariate analysis.
从一组预测变量准确预测疾病进展是临床工作的一个重要方面。对于二元结局，经典方法是开发预后逻辑回归（LR）模型。另外，有人提出了机器学习算法，其中人工神经网络（ANN）在过去几十年中变得很流行。尽管一些研究比较了LR和ANN模型的预测准确性，但对其方法学质量也存在一些担忧。我们的比较基于两个大型独立数据集，具有可以进行精细模型开发和独立验证的优势。
从德国卒中数据库中，使用了一个学习数据集，其中包括1754例前瞻性招募的急性缺血性卒中患者。利用LR和ANN开发了两个预后模型，预测100天后功能独立性恢复和生存情况。将所得模型应用于对1470例急性缺血性卒中患者进行分类；该测试数据集是独立于学习数据集收集的。确定测试数据中的错误率，并计算算法之间错误率的差异及95%置信区间。
对于大多数预后模型，测试数据中的错误率低于40%。除了预测完全恢复与未完全恢复或死亡患者的模型外，算法之间没有差异（错误率差异=4.01%[2.10 - 5.96%]，p = 0.0001）。
认真应用的LR仍然是预后建模的金标准；然而，ANN可以作为一种替代的自动化“快速简便”多变量分析方法。
