Multiclass classification under prioritized error control and specific error costs with applications to dementia classification

优先错误控制和特定错误成本下的多类分类及其在痴呆症分类中的应用

• 批准号: 10474461
• 负责人: Yang Feng
• 金额: $ 23.91万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10474461
• 关键词: Algorithms; Alzheimer' s Disease; Classification; Clinical; Clinical Trials; Computer software; Data; Dementia; Dementia with Lewy Bodies; Development; Discriminant Analysis; Disease; Early Diagnosis; Feasibility Studies; Frontotemporal Dementia; Inequality; Investigation; Lead; Learning; Literature; Logistic Regressions; Methodology; Methods; Parkinson Disease; Patients; Performance; Persons; Pharmaceutical Preparations; Probability; Property; Recommendation; Research; Sample Size; Specific qualifier value; Symptoms; Syndrome; Therapeutic Human Experimentation; Time; Vascular Dementia; Work; accurate diagnosis; base; clinical diagnosis; convolutional neural network; cost; design; drug discovery; effective therapy; flexibility; learning classifier; novel therapeutics; random forest; relative cost; simulation; support vector machine; symptom treatment

Project Summary Fifty million people worldwide have dementia, and there are nearly 10 million new cases every year. The subtypes of dementia include Alzheimer's disease (AD), vascular dementia, Parkinson's disease (PD), dementia with Lewy bodies, and a group of diseases that contribute to frontotemporal dementia. Early and accurate diagnosis of the dementia cause is crucial because it can lead to the timely provision of symptomatic treatment and avoidance of medications that may worsen symptoms and assist in developing and evaluating new drugs and gaining access to effective treatments when they become available. There has thus been extensive research on developing accurate classifiers (e.g., linear discriminant analysis, support vector machines, multiclass logistic regression, random forests, boosting, convolutional neural network) to automatically classify dementia into different classes. In most existing work, the classifiers are designed to maximize overall accuracy. Since different types of classification error may have different consequences (costs), it is highly desirable to develop multiclass classifiers with prioritized error control. There is a rich literature on binary classifiers that minimize the false negative rate, with false positive rate controlled under a particular level. A prominent example is the Neyman-Pearson classification framework. However, the extension of the framework to multiclass classification, in conjunction with the desired prioritized error controls, while of vital importance, remains largely unknown. This project fills this gap by developing a general framework for multiclass classification with controls for misclassification errors, while imposing various (relative) costs for another set of misclassification error types. This can be viewed as a unification of cost-sensitive learning and the Neyman-Pearson classification in the multiclass setting. The new methodological development will optimize clinical diagnosis in the multiclass setting and expedite drug discovery through more efficient clinical trials. Our specific aims are: Aim 1. To propose a flexible framework that includes prioritized error control requirements as well as costs for various classification error types, and develop an efficient umbrella algorithm to solve the associated constrained optimization problem. Aim 2. To study the feasibility of the optimization problem and the properties of the umbrella algorithm. Aim 3. To evaluate the proposed algorithm via extensive simulation studies, apply it to dementia subtype classification, and develop a publicly available R package.

项目摘要 全世界有5000万人患有痴呆症，每年有近1000万新病例。的 痴呆的亚型包括阿尔茨海默病（AD）、血管性痴呆、帕金森病（PD）， 路易体痴呆和一组导致额颞叶痴呆的疾病。早期和 准确诊断痴呆症的原因是至关重要的，因为它可以导致及时提供对症治疗。 治疗和避免可能使症状恶化并有助于发展和评估的药物 新的药物和获得有效的治疗，当他们成为可用。因此已经 关于开发精确分类器的广泛研究（例如，线性判别分析，支持向量机 机器，多类逻辑回归，随机森林，提升，卷积神经网络）， 自动将痴呆症分为不同的类别。在大多数现有的工作中，分类器被设计为 最大限度地提高整体精度。由于不同类型的分类错误可能会产生不同的后果 （成本），非常需要开发具有优先化错误控制的多类分类器。有丰富 关于最小化假阴性率的二进制分类器的文献，其中假阳性率控制在 特殊水平。一个突出的例子是奈曼-皮尔逊分类框架。但 将框架扩展到多类分类，结合所需的优先级错误控制， 虽然至关重要，但仍在很大程度上未知。 这个项目填补了这一空白，开发了一个通用的框架，多类分类与控制， 错误分类错误，同时为另一组错误分类错误类型强加各种（相对）成本。 这可以被视为成本敏感学习和Neyman-Pearson分类的统一。 多类设置。新的方法学发展将优化临床诊断的多类 通过更有效的临床试验来建立和加速药物发现。我们的具体目标是： 目标1。提出一个灵活的框架，其中包括优先考虑的错误控制要求以及成本， 各种分类错误类型，并开发了一种高效的伞形算法来解决相关问题 约束优化问题 目标2.研究了该优化问题的可行性和伞形算法的性质。 目标3.为了通过广泛的模拟研究来评估所提出的算法，将其应用于痴呆亚型 分类，并开发一个公开可用的R包。