Medical Advice from Glaucoma Informatics (MAGI)

青光眼信息学 (MAGI) 的医疗建议

• 批准号: 6830123
• 负责人: MICHAEL H GOLDBAUM
• 金额: $ 58.93万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-30 至 2006-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6830123
• 关键词: clinical research; diagnosis design /evaluation; diagnosis quality /standard; early diagnosis; eye disorder diagnosis; glaucoma; glaucoma test; human subject; neural information processing; neuropathology; visual fields

DESCRIPTION (provided by applicant): The project, Medical Advice from Glaucoma Informatics (MAGI), seeks to improve glaucoma diagnosis and management with state-of-the-art machine learning classifiers. These classifiers will automate the interpretation of standard automated perimetry (SAP), newer visual field tests, and structural tests for glaucoma in the general population and in stratified glaucoma populations. Phase 1 will complete the feasibility testing already underway. Phase 2 will apply the refined methods to a wider set of glaucoma testing problems.The management of glaucoma depends on a series of classifications. The glaucoma provider classifies tests as normal or indicative of glaucoma. The clinician then determines whether an eye has glaucoma or has had progression. Assembling these classifications, the provider makes decisions about management. Automated test interpreters, either as part of the testing machine or as a computer-based resource, can aid glaucoma providers with real-time interpretations. The research we propose takes advantage of our extensive data sets and builds on the ongoing research in our laboratories.Statistical classifiers, Bayesian nets, machine learning classifiers, and expert systems represent different types of classifiers with diverse properties. Machine learning classifiers can perform exceptionally well at identifying classes, even when the data are complex and have dependencies. We will test and select the optimal machine learning classifier for diagnosis. We will further improve classifier performance and determine feature utility by optimizing the feature set visual field tests are time consuming and stressful. We will streamline the tests by removing unimportant test points.Even with decades of experience, there is uncertainty with regard to the evaluation of the SAP. There is less accumulated knowledge about non-standard tests, such as short-wavelength automated perimetry, nerve fiber layer thickness, or optic nerve head topography. Machine classifiers may learn how to interpret nonstandard tests better. We will go beyond STATPAC's capabilities with classifiers that have learned to interpret SAP, nonstandard visual field tests, structural glaucoma tests, and STATPAC plots in the general population and in patients stratified by race, family history, and other information available at the time of the test.Conversion of suspects to glaucoma and progression of glaucoma cannot yet be predicted from tests. We will develop classifiers for these predictions. Classifiers will be designed to diagnose early glaucoma, detect early progression, and identify glaucomatous eyes at risk of progression.Unsupervised learning provides cluster analysis that can determine distinct groups with members in some way similar from the test data. In an effort to discover new and use useful information with unsupervised learning, we will mine our data in visual function and structural tests for glaucoma and in specific combinations of population groups.

描述（由申请人提供）：该项目是青光眼信息学（MAGI）的医疗建议，旨在通过最先进的机器学习分类器来改善青光眼诊断和管理。这些分类器将自动化标准自动化外围（SAP），较新的视野测试以及一般人群中青光眼和分层青光眼种群中青光眼的结构测试的解释。第1阶段将完成已经进行的可行性测试。第2阶段将把精致的方法应用于更广泛的青光眼测试问题。青光眼的管理取决于一系列分类。青光眼提供者将测试归类为正常或指示青光眼。然后，临床医生确定眼睛是否具有青光眼或进展。组装这些分类，提供商就管理层做出决定。自动测试口译员是测试机的一部分，也可以作为基于计算机的资源，可以帮助具有实时解释的青光眼提供者。我们提出的研究利用了我们的广泛数据集，并基于实验室正在进行的研究。统计分类器，贝叶斯网，机器学习分类器和专家系统代表具有不同属性的不同类型的分类器。即使数据很复杂并且具有依赖性，机器学习分类器也可以在识别类方面表现出色。我们将测试并选择最佳机器学习分类器进行诊断。我们将进一步提高分类器性能并通过优化特征集的视野测试来确定功能实用程序，这是耗时且压力很大。我们将通过删除不重要的测试点来简化测试。即使有数十年的经验，对SAP的评估存在不确定性。关于非标准测试的累积知识较少，例如短波长度的周围，神经纤维层的厚度或视神经头部形状。机器分类器可能会学习如何更好地解释非标准测试。我们将超越STATPAC的能力，这些分类器在一般人群中学会了解释SAP，非标准视野测试，结构青光眼测试以及StatPAC图，以及根据种族，家族史和其他测试时可用的其他信息分层的患者。我们将开发这些预测的分类器。分类器将被设计为诊断早期青光眼，检测早期进展并识别出具有进展风险的青光眼眼睛。不接受的学习提供了聚类分析，可以以与测试数据相似的方式确定与成员的不同组。为了发现新的和使用无监督学习的有用信息，我们将在青光眼和人群群体的特定组合中挖掘我们的数据和结构性测试。