Using Machine Learning to Improve the Predictive Accuracy of Disease Cure

使用机器学习提高疾病治疗的预测准确性

• 批准号: 10654253
• 负责人: SUVRA PAL
• 金额: $ 45.21万
• 依托单位: UNIVERSITY OF TEXAS ARLINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10654253
• 关键词: Address; Adjuvant Therapy; Algorithms; Area; Biological; Cessation of life; Characteristics; Classification; Clinical; Clinical Trials; Complex; Computer software; Data; Data Set; Diagnosis; Disease; Disease Progression; Event; Goals; Health Professional; Incidence; Infection; Machine Learning; Methods; Modeling; Modernization; Motivation; Nature; Neoplasm Metastasis; Patients; Pattern; Probability; Procedures; ROC Curve; Recurrence; Research; Statistical Models; Structure; Testing; Therapeutic; Training; Validation; Variant; cost; expectation; experience; feature selection; flexibility; hazard; high dimensionality; high risk; improved; learning algorithm; machine learning prediction; model building; multidimensional data; next generation; novel; novel strategies; optimal treatments; patient population; precision medicine; predictive modeling; screening; support vector machine; survival prediction; treatment strategy

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.

抽象的 随着筛查，诊断和治疗方面的最新进展，在早期发现了许多疾病，并且 患有这些疾病的患者中有很大的比例是临床治愈的。也就是说，这些患者永远不会 经历因主要疾病而导致的复发，转移或死亡。在患有早期疾病的患者中 根据治疗的预处理特征，尽早确定治愈患者在临床上很重要，以便这些患者 可以保护患者免受高强度治疗的额外风险。同样，识别未经意的患者 早期也很重要，以便可以及时对待他们的疾病，直到其疾病进入高级阶段 治疗选择相当有限。这种识别对于临床试验以开发有效调整也至关重要 疗法。这是对可以获取患者生存数据和任何可用的任何可用预测模型的巨大需求 有关患者相关特征（或特征）作为简单输入的信息，并预测已治愈或未经许可的状态 精度高的患者。现有能够进行此类预测的最新模型带有几个缺点 这使它们难以满足对高级应用程序不断增长的需求。这些包括缺乏生物学 与相关 估计程序，无法充分处理高维数据，这导致了预测性的暗示性 治愈/纯净的准确性，模型的不可用以及相关方法作为现成软件 其中大多数包装都需要丰富的编程经验才能成功实施。提议 研究旨在通过发展下一代模型来解决理性问题，以改进 复杂性和较低的计算成本，以高度准确地预测存在 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集成 捕获数据中的复杂模式。我们假设捕获这种复杂模式将大大改善 治愈的预测准确性，也将改善未经许可的生存分布 患者。特别是，提出了以下特定目标。目标1：开发新型的支持向量机器 - 基于预测模型，可以将患者人群捕获为治愈和未经许可患者的混合物；目标2：到 开发可以处理高维数据的新计算有效估计和特征选择方法； 目标3：开发使用现有患者生存数据验证拟议模型的新方法并开发R 免费和非拟合使用的软件包。成功完成这项研究将有助于治疗作业 以及为患者的整体益处开发有效调整疗法的需求。