Prognostic prediction is the task of estimating a patient’s risk of disease development based on various predictors. Such prediction is important for healthcare practitioners and patients because it reduces preventable harm and costs. As such, a prognostic prediction model is preferred if: 1) it exhibits encouraging performance and 2) it can generate intelligible rules, which enable experts to understand the logic of the model’s decision process. However, current studies usually concentrated on only one of the two features. Toward filling this gap, in the present study, we develop a novel knowledge-aware Bayesian model taking into consideration accuracy and transparency simultaneously. Real-world case studies based on four years’ territory-wide electronic health records are conducted to test the model. The results show that the proposed model surpasses state-of-the-art prognostic prediction models in accuracy and c-statistic. In addition, the proposed model can generate explainable rules.

中文翻译：

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用于疾病预后预测的合并症知识感知模型


预后预测是根据各种预测因素估计患者疾病发展风险的任务。这种预测对于医疗保健从业者和患者来说非常重要，因为它可以减少可预防的伤害和成本。因此，在以下情况下，预后预测模型是首选：1）它表现出令人鼓舞的性能，2）它可以生成易于理解的规则，使专家能够理解模型决策过程的逻辑。然而，目前的研究通常只集中于这两个特征之一。为了填补这一空白，在本研究中，我们开发了一种新颖的知识感知贝叶斯模型，同时考虑了准确性和透明度。我们根据四年全港电子健康记录进行真实案例研究，以测试该模型。结果表明，所提出的模型在准确性和 c 统计量方面超越了最先进的预后预测模型。此外，所提出的模型可以生成可解释的规则。