Model for prediction of noise-induced hearing loss

噪声引起的听力损失的预测模型

• 批准号: 6753927
• 负责人: Roger P Hamernik
• 金额: $ 13.84万
• 依托单位: PLATTSBURGH STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-01 至 2006-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6753927
• 关键词: chinchilla; computational neuroscience; computer program /software; computer system design /evaluation; mathematical model; model design /development; noise induced deafness; noise pollution

DESCRIPTION: The objective of the proposed work is to develop a prediction model using a statistical learning machine (SLM), which includes an artificial neural network (ANN), a support vector machine (SVM), and a hybrid of ANN and SVM, that will predict the auditory consequences of excessive noise exposure in a chinchilla model. The SLM model will be fed training data from our existing database consisting of noise exposure metrics and audiometric/histological/otoacoustic emission data acquired from previously exposed animals. Once trained, the SLM model will be able to predict the auditory consequences of exposure to any noise environment characterized by the noise metrics and biological variables of the animals that are input to the model. There are two phases to this research. The first phase is the design of the system structure and implementation software (Year 1). The second phase involves training the system using an extensive database consisting of more than 2500 subjects on which comprehensive exposure and audiometric/ histological data are available (Year 2). The training period will be an iterative process in which the SLM will be modified as training proceeds. The predictions of the SLM model can also be used to design experimental conditions from which the model can be experimentally tested. The successful demonstration of the application of a statistical learning machine to the prediction of noise-induced auditory effects has considerable potential for application to the assessment of industrial and military noise environments for the protection of hearing in humans, and can result in a considerable savings in the amount of work/resources that are needed to develop new and improved noise standards. Specifically, given the many similarities in the response to noise of the chinchilla and the human, the model can be used to identify combinations of parameters of an exposure that are important determinants of damage that would also be applicable to human exposure conditions. The chinchilla model can be used as a template or a guide for developing a human model possibly from some of the existing human data from which current standards were developed.

产品说明：所提出的工作的目标是开发一个预测模型，使用统计学习机（SLM），其中包括人工神经网络（ANN），支持向量机（SVM），人工神经网络和SVM的混合，这将预测的听觉后果过度噪声暴露在一个栗鼠模型。SLM模型将从我们现有的数据库中获得训练数据，这些数据库包括噪声暴露指标和从先前暴露的动物中获得的听力/组织学/耳声发射数据。 一旦经过训练，SLM模型将能够预测暴露于任何噪声环境的听觉后果，其特征在于输入模型的动物的噪声度量和生物变量。这项研究分为两个阶段。第一阶段是系统结构和实施软件的设计（第一年）。第二阶段涉及使用由2500多名受试者组成的广泛数据库对系统进行培训，这些受试者可获得全面的暴露和听力测定/组织学数据（第2年）。培训期将是一个反复的过程，可持续土地管理将随着培训的进行而修改。SLM模型的预测还可以用于设计实验条件，从该实验条件可以对模型进行实验测试。统计学习机在预测噪声引起的听觉效应中的应用的成功示范对于应用于工业和军事噪声环境的评估以保护人类的听力具有相当大的潜力，并且可以导致开发新的和改进的噪声标准所需的大量工作/资源的相当大的节省。具体而言，由于许多相似之处的灰鼠和人类的噪声的反应，该模型可以用来确定组合的参数的曝光是重要的决定因素的损害，也将适用于人类的暴露条件。龙猫模型可以用作模板或指南，用于开发人类模型，可能是从一些现有的人类数据中开发出当前的标准。