Objective

Very often the performance of a Bayesian Network (BN) is affected when applied to a new target population. This is mainly because of differences in population characteristics. External validation of the model performance on different populations is a standard approach to test model’s generalisability. However, a good predictive performance is not enough to show that the model represents the unique population characteristics and can be adopted in the new environment.

Methods

In this paper, we present a methodology for updating and recalibrating developed BN models – both their structure and parameters - to better account for the characteristics of the target population. Attention has been given on incorporating expert knowledge and recalibrating latent variables, which are usually omitted from data-driven models.

Results

The method is successfully applied to a clinical case study about the prediction of trauma-induced coagulopathy, where a BN has already been developed for civilian trauma patients and now it is recalibrated on combat casualties.

Conclusion

The methodology proposed in this study is important for developing credible models that can demonstrate a good predictive performance when applied to a target population. Another advantage of the proposed methodology is that it is not limited to data-driven techniques and shows how expert knowledge can also be used when updating and recalibrating the model.

中文翻译：

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更新和重新校准新目标人群的因果概率模型

 客观的

当贝叶斯网络 (BN) 应用于新的目标人群时，其性能常常会受到影响。这主要是由于人口特征的差异。在不同人群上对模型性能进行外部验证是测试模型普遍性的标准方法。然而，良好的预测性能并不足以表明该模型代表了独特的群体特征并且可以在新环境中采用。

 方法

在本文中，我们提出了一种更新和重新校准已开发的 BN 模型（包括其结构和参数）的方法，以更好地考虑目标人群的特征。人们关注的是整合专家知识和重新校准潜在变量，这些变量通常在数据驱动模型中被忽略。

 结果

该方法已成功应用于预测创伤引起的凝血病的临床案例研究，其中已经为平民创伤患者开发了 BN，现在又根据战斗伤亡情况进行了重新校准。

 结论

本研究提出的方法对于开发可靠的模型非常重要，这些模型在应用于目标人群时可以表现出良好的预测性能。所提出的方法的另一个优点是它不限于数据驱动技术，并且展示了在更新和重新校准模型时如何使用专家知识。