A two-stage machine learning framework to predict heart transplantation survival probabilities over time with a monotonic probability constraint
A two-stage machine learning framework to predict heart transplantation survival probabilities over time with a monotonic probability constraint
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DOI：
10.1016/j.dss.2020.113363
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发表时间：
2020-10-01
影响因子：
7.5
通讯作者：
Megahed, Fadel M.
中科院分区：
文献类型：
--
作者：
Dolatsara, Hamidreza Ahady;Chen, Ying-Ju;Megahed, Fadel M.
本文的总体目标是开发一个建模框架，可用于获得个性化的，数据驱动的和单调约束的概率曲线。这项研究的动机是提高器官移植结果的预测，这可以通知器官分配协议，移植后护理途径和临床资源利用的更新的重要问题。为了实现我们的总体目标和激励问题，我们提出了一个新的基于两阶段机器学习的框架，用于获得随时间变化的单调概率。第一阶段使用标准方法，使用独立的机器学习模型来预测每个感兴趣的时间段的移植结果。在第二阶段中，我们使用保序回归校准随时间推移的生存概率。为了展示我们的框架的实用性，我们将其应用于1987年至2016年美国心脏移植的国家登记处。第一阶段产生1 - 10年的受试者工作曲线下面积（AUC）为0.60 - 0.71。虽然1年预测AUC结果与文献中报告的结果相当，但我们的10年AUC（0.70）高于当前最新技术水平的结果。更重要的是，我们证明了应用保序回归来校准每个患者在10年内的生存概率可以保证单调性，同时利用了机器学习模型的数据驱动和个性化特性。为了促进未来的研究，我们的代码和分析在GitHub上公开。此外，我们创建了一个名为“H-TOP：心脏移植结果预测器”的网络应用程序，以鼓励实际应用。
The overarching goal of this paper is to develop a modeling framework that can be used to obtain personalized, data-driven and monotonically constrained probability curves. This research is motivated by the important problem of improving the predictions for organ transplantation outcomes, which can inform updates made to organ allocation protocols, post-transplantation care pathways, and clinical resource utilization. In pursuit of our overarching goal and motivating problem, we propose a novel two-stage machine learning-based framework for obtaining monotonic probabilities over time. The first stage uses the standard approach of using independent machine learning models to predict transplantation outcomes for each time-period of interest. In the second stage, we calibrate the survival probabilities over time using isotonic regression. To show the utility of our framework, we applied it on a national registry of U.S. heart transplants from 1987 to 2016. The first stage produces an area under the receiver operating curve (AUC) between 0.60 and 0.71 for years 1-10. While the 1-year prediction AUC result is comparable to the reported results in the literature, our 10-year AUC of 0.70 is higher than the current state-of-the-art results. More importantly, we show that the application of isotonic regression to calibrate the survival probabilities for each patient over the 10-year period guarantees monotonicity, while capitalizing on the data-driven and individualized nature of machine learning models. To promote future research, our code and analysis are publicly available on GitHub. Furthermore, we created a web app titled "H-TOP: Heart Transplantation Outcome Predictor" to encourage practical applications.