Model for prediction of noise-induced hearing loss

描述：本研究的目标是利用统计学习机（SLM）建立一个预测模型，该模型包括人工神经网络（ANN）、支持向量机（SVM）以及人工神经网络和支持向量机的混合模型，该模型将在栗鼠模型中预测过度噪声暴露的听觉后果。SLM模型将从我们现有的数据库中输入训练数据，该数据库包括噪声暴露度量和从先前暴露的动物中获得的听力/组织学/耳声发射数据。一旦训练，SLM模型将能够预测暴露于任何噪声环境的听觉后果，这些噪声环境的特征是输入到模型中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设计和实施软件设计（一年级）。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，该数据库可提供全面的暴露和听力/组织学数据（第2年）。培训期间将是一个迭代过程，随着培训的进行，SLM将被修改。SLM模型的预测也可以用来设计实验条件，从这些条件中可以对模型进行实验测试。统计学习机应用于预测噪声引起的听觉效果的成功演示，具有相当大的应用潜力，可用于评估工业和军事噪声环境，以保护人类听力，并可大大节省开发新的和改进的噪声标准所需的工作量/资源。具体来说，考虑到栗鼠和人类对噪音的反应有许多相似之处，该模型可以用来确定暴露参数的组合，这些参数是损害的重要决定因素，也适用于人类的暴露条件。栗鼠模型可以作为模板或指南，可能从现有的一些人类数据中开发人类模型，目前的标准就是从这些数据中开发的。

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.