Hospital decision-makers use predictive models to proactively manage risk of readmission for discharged patients. While predictions from classification models are easily integrated into decision-making processes, it is unclear how to best integrate predictions of the evolution of risk from time-to-event models. We propose a method for summarising predictions of risk over time that produces interpretable components for use in a variety of decision-making processes. The proposed method summarises predictions of risk over time (hazard functions) by approximating them with a parametric smoother. The components of the smoothed approximation can then serve as the basis for decision-making. To demonstrate the proposed summarisation method, we apply it in the specific case of a previously published model for patients discharged from a large teaching hospital on the Gold Coast, Australia. In this context, we describe how the summaries produced by the method could be used to estimate time until a patient reaches a stable, persistent risk level or to stratify patients according to risks of readmission in excess of patient-specific baselines. Our method is anticipated to be valuable in and outside of healthcare for settings where the evolution of risk is important, with specific examples including post-transplantation risk and reinjury risks.

中文翻译：

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将危险函数分解为可解释的再入院风险成分


医院决策者使用预测模型主动管理出院患者的再入院风险。虽然分类模型的预测很容易集成到决策过程中，但尚不清楚如何最好地集成事件时间模型的风险演变预测。我们提出了一种总结随时间变化的风险预测的方法，该方法产生可解释的组件以用于各种决策过程。所提出的方法通过使用参数平滑器近似来总结随时间变化的风险预测（风险函数）。然后，平滑近似的分量可以作为决策的基础。为了演示所提出的总结方法，我们将其应用于之前发布的澳大利亚黄金海岸一家大型教学医院出院患者模型的具体案例中。在这种情况下，我们描述了如何使用该方法生成的摘要来估计患者达到稳定、持续风险水平的时间，或根据超过患者特定基线的再入院风险对患者进行分层。我们的方法预计在医疗保健内外对于风险演变很重要的环境很有价值，具体示例包括移植后风险和再损伤风险。