Accurate Survival Prediction

许多任务都涉及到估计事件发生前的时间：例如，医生通常需要预测患者**死亡(或复发，或康复)的时间；零售公司希望预测**客户离开(“客户流失”)或员工辞职的时间。生存分析领域已经**产生了很多工具来分析这些数据，但大多数产生的分数可以用来对患者进行排名**，而不是对生存时间的明确预测。**在之前的研究中，我们开发了一种称为患者特定生存预测(PSSP)的方法，我们**使用它来预测任何期望的感兴趣时间的生存概率，以及计算预期的**生存时间。此外，我们还开发了评估生存分析方法准确性的指标(生存数据的一个**挑战是，我们通常只知道实例生存时间的下限，而不知道**确切的生存时间，这使得评估变得困难)。**加拿大皇家银行很高兴将PSSP(和其他生存分析方法)应用于信用风险领域**。具体地说，加拿大皇家银行有兴趣将该项目的结果应用于其分期付款贷款和汽车金融产品，以努力预测个人可能在什么时候拖欠贷款。在计算承销**贷款的可行性时，预测**客户何时违约或预付贷款是至关重要的。**拟议项目的目标是探索如何优化预测生存时间的准确性，并**探索使用集成模型来改进特定**信用风险领域内的个人生存分析方法。

Many tasks involves estimating the time until an event: eg, doctors often need to predict the time until a patient**will die (or until she has a relapse, or until she recovers); retail companies want to predict the time until a**customer will leave ("customer churn"), or until an employee will resign. The field of survival analysis has**produced a great many tools for analyzing this data, but most generate a score that can be used to rank patients**rather than an explicit prediction of survival time.**In previous research, we've developed a method called Patient-Specific Survival Prediction (PSSP), which we**use to predict the probability of survival at any desired time of interest, as well as compute the expected**survival time. Further, we have developed metrics to evaluate the accuracy of survival analysis methods (a**challenge of survival data is that frequently we know only a lower-bound of an instance's survival time, but not**the exact survival time, making evaluation difficult).**The Royal Bank of Canada is excited to apply PSSP (and other survival analysis methods) to the area of credit**risk. Specifically, RBC is interested in applying the results of this project to its installment loan and auto**finance products in the effort of predicting at what time an individual is likely to default on a loan. Predicting**when a client will default or pre-pay the loan are paramount in calculating the viability of underwriting the**loan.**The goals of the proposed project are to explore how to optimize accuracy of predicted survival time, and to**explore the use of ensemble models to improve upon individual survival analysis methods, within the specific**domain of credit risk.