With the ever-growing complexity of primary health care system, proactive patient failure management is an effective way to enhancing the availability of health care resource. One key enabler is the dynamic prediction of time-to-event outcomes. Conventional explanatory statistical approach lacks the capability of making precise individual level prediction, while the data adaptive binary predictors does not provide nominal survival curves for biologically plausible survival analysis. The purpose of this article is to elucidate that the knowledge of explanatory survival analysis can significantly enhance the current black-box data adaptive prediction models. We apply our recently developed counterfactual dynamic survival model (CDSM) to static and longitudinal observational data and testify that the inflection point of its estimated individual survival curves provides reliable prediction of the patient failure time.

翻译：随着初级保健系统日益复杂,积极主动的病人失灵管理是增加保健资源的有效途径。一个关键的促进因素是动态预测时间到活动结果。常规解释性统计方法缺乏准确个人水平预测的能力,而数据适应性二进制预测器没有为生物上可信的生存分析提供名义生存曲线。本篇文章的目的是说明解释性生存分析知识可以大大加强目前的黑盒数据适应性预测模型。我们将我们最近开发的反事实动态生存模型(CDSM)应用于静态和纵向观察数据,并证明估计个人生存曲线的偏差点提供了对病人失败时间的可靠预测。