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，许多疾病都能在早期被发现并被诊断为fi。 这些疾病的患者中有很大一部分是临床治愈的。fi。也就是说，这些患者永远不会 曾因原发病复发、转移或死亡。在早期疾病患者中， 根据患者治疗前的特点，及早发现治愈的患者具有重要的临床意义。 患者可以免受高强度治疗的额外风险。同样，识别未治愈的患者 及早治疗也很重要，这样他们就可以在疾病进展到晚期之前得到及时的治疗 治疗选择相当有限。这种相同的fi阳离子对于开发有效佐剂的临床试验也是至关重要的。 治疗。因此，非常需要一种能够获取患者生存数据和任何可用数据的预测性模型 关于患者相关特征(或特征)的信息作为简单的输入，并预测治愈或未治愈的状态 患者的准确率很高。现有的能够进行这种预测的最先进的模型有几个缺点 这使得它们很难满足日益增长的高级应用需求。其中包括缺乏生物学上的 动机和限制性模型假设、非稳健性和全局收敛问题 估计过程，无法有效地处理高维数据，从而导致预测不准确 治愈/不确定的准确性，以及作为现成软件的模型和相关方法的不可用 包，其中大多数需要丰富的编程经验才能成功实现。建议数 研究试图通过开发下一代模型来解决上述问题，该模型基于 复杂性和较低的计算成本，用于在存在以下情况时高度准确地预测固化或未固化状态 高维数据。这里的新想法是将机器学习与现代预测统计模型相结合 来捕捉数据中的复杂模式。我们假设，捕获这种复杂的模式将大大提高 治愈的预测准确性也将导致对未治愈的生存分布的更好的预测 病人。具体地说，提出了以下规范fic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已治愈和未治愈患者的混合体；目标2： 开发能够处理高维数据的新的计算量有效的fi估计和特征选择方法； 目的3：开发新的方法，利用现有的患者生存数据来验证所提出的模型，并开发R 免费和非专业fi测试使用的软件包。这项研究的成功完成将有助于进行治疗任务 以及为患者的整体利益开发有效的辅助治疗的必要性。

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.