Using Machine Learning to Improve the Predictive Accuracy of Disease Cure

摘要 随着筛查、诊断和治疗的最新进展，许多疾病在早期阶段就被识别出艾德， 很大一部分患有这些疾病的病人在临床上得到治愈。也就是说，这些病人永远不会 复发、转移或因原发病死亡。在患有早期疾病的患者中， 根据治疗前的特征，早期识别治愈的患者在临床上很重要， 可以保护患者免受高强度治疗的额外风险。同样，识别未治愈的患者 早期也很重要，这样他们就可以在疾病发展到晚期之前得到及时治疗， 治疗选择相当有限。这种识别对于开发有效佐剂的临床试验也至关重要 治疗因此，存在对可以采用患者生存数据和任何可用的预测模型的巨大需求。 关于患者相关特性（或特征）的信息作为简单输入，并预测患者的治愈或未治愈状态。 患者准确率高。能够进行这种预测的现有最先进的模型具有几个缺点 这使得它们难以满足对高级应用的日益增长的需求。其中包括缺乏生物 动机和限制性模型假设，非鲁棒性和全局收敛问题与相关的 估计程序，无法有效处理高维数据，导致预测不准确 固化/不固化的准确性，以及模型和相关方法作为即用型软件不可用 软件包，其中大多数需要丰富的编程经验才能成功实现。拟议 研究试图通过开发下一代模型来解决上述问题， 复杂性和较低的计算成本，用于在存在以下情况下高度准确地预测固化或未固化状态： 高维数据这里的新颖想法是将机器学习与现代预测统计模型相结合 捕捉数据中的复杂模式。我们假设，捕捉这种复杂的模式将大大提高 并且还将导致未固化的存活分布的改进的预测 患者特别提出了以下具体目标。目标1：开发一种新的支持向量机- 基于预测模型，可以将患者群体捕获为治愈和未治愈患者的混合物;目标2： 开发新的计算有效的估计和特征选择方法，可以处理高维数据; 目的3：开发新的方法，使用现有的患者生存数据验证所提出的模型，并开发R 免费和非盈利使用的软件包。成功完成本研究将有助于治疗分配 以及需要开发有效的辅助疗法以使患者整体贝内。

Abstract With recent advancements in screening, diagnosis and treatment, many diseases are identified at an early stage and a significant proportion of patients suffering from these diseases are clinically cured. That is, these patients will never experience recurrence, metastasis or death due to the primary disease. Among patients with early-stage diseases, it is clinically important to identify cured patients early, based on their pre-treatment characteristics, so that these patients can be protected from the additional risks of high-intensity treatments. Similarly, identifying uncured patients early is also important so that they can be treated timely before their diseases progress to advanced stages for which therapeutic options are rather limited. Such identification is also crucial for clinical trials to develop effective adjuvant therapies. Thus, there is an immense need for a predictive model that can take patient survival data and any available information on patient-related characteristics (or features) as simple inputs and predict the cured or uncured status of patients with high accuracy. Existing state-of-the-art models capable of such prediction come with several drawbacks that make them hard to meet the increasing needs for advanced applications. These include the lack of biological motivation and restrictive model assumptions, non-robustness and global convergence problems with the associated estimation procedures, inability to efficiently handle high-dimensional data which leads to impreciseness in predictive accuracies of cure/uncure, and unavailability of the models and the associated methods as ready-to-use software packages with most of them requiring rich programming experience for successful implementation. The proposed research seeks to address the aforementioned issues by developing a next generation model, based on decreased complexity and lower computational cost, for highly accurate prediction of cured or uncured status in the presence of high-dimensional data. The novel idea here is to integrate machine learning with modern predictive statistical model to capture complex patterns in the data. We hypothesize that capturing such complex patterns will greatly improve the predictive accuracy of cure and will also result in improved prediction of the survival distribution of the uncured patients. In particular, the following specific aims are proposed. Aim 1: To develop a novel support vector machine- based predictive model that can capture the patient population as a mixture of cured and uncured patients; Aim 2: To develop new computationally efficient estimation and feature selection methods that can handle high-dimensional data; Aim 3: To develop new method for validating the proposed model using existing patient survival data and develop R software package for free and non-profit use. Successful completion of this research will aid in treatment assignment and the need to develop effective adjuvant therapies for the overall benefit of patients.