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、诊断和治疗的最新进展，许多疾病可以在早期发现并进行治疗。 相当一部分患有这些疾病的患者在临床上得到治愈，也就是说，这些患者永远无法治愈。 因原发病复发、转移或死亡的患者。 在临床上，根据治疗前的特征及早识别治愈的患者非常重要，以便这些患者能够 可以保护患者免受高强度治疗的额外风险。同样，可以识别未治愈的患者 早期也很重要，以便在疾病进展到晚期之前及时治疗。 治疗选择相当有限，这种鉴定对于开发有效佐剂的临床试验也至关重要。 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模型。 有关患者相关特征（或特征）的信息作为简单输入并预测治愈或未治愈状态 能够进行此类预测的现有最先进模型存在一些失败。 这使得它们难以满足对先进应用日益增长的需求，其中包括缺乏生物技术。 动机和限制性模型假设、非鲁棒性和相关的全局收敛问题 估计程序，无法有效处理高维数据，导致预测不精确 治愈/未治愈的准确性，以及模型和相关方法作为即用型软件的可用性 其中大多数软件包需要丰富的编程经验才能成功实施。 研究旨在通过开发下一代模型来解决上述问题，该模型基于减少的 复杂性和较低的计算成本，可在存在固化或未固化状态的情况下高度准确地预测 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。 捕获数据中的复杂模式我们利用捕获这种复杂模式将大大提高。 治愈的预测准确性，也将改善对未治愈者生存分布的预测 特别是，提出了以下具体目标：开发一种新型支持向量机。 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； 目标 3：开发新方法，使用现有患者生存数据验证所提出的模型并开发 R 免费和非营利性使用的软件包。成功完成这项研究将有助于治疗分配。 以及为了患者的整体利益而开发有效的辅助疗法的需要。