Background

Recurrent venous thromboembolism (RVTE) is a multifactorial disease with occurrence rates which vary from 13 % to 25 % in 5 years after the initial event. Once a patient the first thrombotic event, the probability of recurrence should be determined, as well as the most adequate anticoagulant therapy. To our knowledge based on the published literature, three statistical models have been proposed to calculate RVTE probability. However, these models present several limitations, such as: limited input variables, lack of external validation and applicability only for patients with a first unprovoked thrombosis. Additionally, some of the models have been recognized to fail in determining RVTE when patients have a low risk of recurrence.

Objective

An alternative procedure in which three Artificial Neural Network (ANN) models were developed to classify which patients will present RVTE based solely on clinical data.

Methods

Data of 39 clinical factors from 235 patients were used to train several ANN structures. The difference among the three models was its inputs. In ANN 1, the inputs were all 39 factors. In ANN 2, 18 factors determined previously as the main predictors of RTVE using Principal Component Analysis (PCA). Finally, in ANN 3, 15 factors combining PCA results with practical aspects. Different number of hidden layers and neurons, and three optimization algorithms were considered. 5-fold cross validation was also performed.

Results

The results showed that all models were capable of performing this task. Different optimization algorithms lead to different results. The best models presented high accuracy. The best structures were 39−10-10−1, 18−10-5−1, and 15−15-10−1 for ANN 1, ANN 2, and ANN 3 models, respectively. The cross-validation showed that the results are consistent.

Conclusions

This work showed that the association of multivariate techniques and ANNs is a powerful tool that can be used to model a complex phenomenon such as RVTE without the restrictions of existing approaches.

Application

After proper validation, these ANN models can be used to help clinicians with decisions regarding VTE treatment.

中文翻译：

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人工神经网络用于预测复发性静脉血栓栓塞症。

背景

复发性静脉血栓栓塞症（RVTE）是一种多因素疾病，在初次事件后的5年内，其发生率从13％到25％不等。一旦患者发生首次血栓事件，就应确定复发的可能性以及最充分的抗凝治疗。据我们所公开的文献资料，已经提出了三种统计模型来计算RVTE概率。但是，这些模型存在一些局限性，例如：输入变量有限，缺乏外部验证以及仅适用于首次无缘血栓形成的患者。另外，当患者的复发风险较低时，某些模型无法确定RVTE。

目的

开发了三种人工神经网络（ANN）模型以仅根据临床数据对哪些患者会出现RVTE进行分类的替代程序。

方法

来自235名患者的39个临床因素的数据被用于训练几种人工神经网络结构。三种模型之间的差异在于其输入。在人工神经网络1中，输入都是39个因素。在人工神经网络2中，先前使用主成分分析（PCA）确定了18个因素作为RTVE的主要预测因子。最后，在人工神经网络3中，结合了PCA结果和实际方面的15个因素。考虑了不同数量的隐藏层和神经元，并考虑了三种优化算法。还进行了5倍交叉验证。

结果

结果表明，所有模型都能够执行此任务。不同的优化算法导致不同的结果。最好的模型具有很高的准确性。对于ANN 1，ANN 2和ANN 3模型，最佳结构分别为39-10-10-1、18-10-5-1和15-15-10-1。交叉验证表明结果是一致的。

结论

这项工作表明，多元技术和人工神经网络的关联是一个功能强大的工具，可用于对诸如RVTE之类的复杂现象进行建模，而不受现有方法的限制。

应用

经过适当验证后，这些ANN模型可用于帮助临床医生做出有关VTE治疗的决策。