A statistical learning model for predicting noise-induced hearing loss in humans

用于预测人类噪声引起的听力损失的统计学习模型

• 批准号: 7682810
• 负责人: Roger P Hamernik
• 金额: $ 33.13万
• 依托单位: PLATTSBURGH STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7682810
• 关键词: statistical; learning; model; predicting; noise

DESCRIPTION (provided by applicant): Existing damage risk criteria for noise exposure are based entirely on an energy metric. Our animal model experiments have shown that energy alone is not sufficient to characterize a complex noise for hearing conservation purposes. These data suggest that energy and the statistical metric kurtosis may constitute necessary and sufficient metrics for the prediction of hearing loss that will result from long-term industrial exposures. This proposal is a continuation of this effort but focuses on the human condition. It is important to verify the animal results with comparable human exposure and hearing loss data. The main aim is to develop a noise measurement/analysis strategy that can be used to estimate the hearing loss that will develop in workers exposed to complex industrial noise* environments. Statistical learning models will be developed to achieve this goal. Model development will require that the following data be obtained: (1) Stable audiograms on a population of highly screened workers exposed to Gaussian and nonGaussian noise environments. (2) Record, archive and analyze the noise waveforms that each subject is exposed to over the course of an entire work shift. The models developed from this database will identify the noise variables that are important in the production of hearing loss and can also be used to develop exposure criteria. In addition, in response to ethical concerns of collecting data from unprotected workers, elements of a hearing conservation program will be introduced. The experimental paradigm will generate data needed for the development of an empirical basis for using in addition to an energy metric to predict the hazards of an exposure, variables that quantify the temporal and peak characteristics of a noise such as the kurtosis, impact interval and peak histograms, and transient durations. Data will be collected in collaboration with researchers at Peking University and include a population of at least 1,250 workers employed in various complex, high noise level industrial settings in China. This noise exposure and cross-sectional audiometric data will constitute a unique database that will fill a NIOSH acknowledged void in the data available for developing exposure criteria for hearing conservation purposes. [*Throughout this proposal our use of the term 'complex noise' refers to non-Gaussian noise, which in the course of a work cycle, may be intermittent, interrupted and of variable level. Non-Gaussian noise is very common in industry and the military where it consists of a background Gaussian noise with embedded high- level transients (impacts or noise bursts).] Existing safety criteria for noise exposure are based entirely on an energy metric. New data indicate that energy alone is not a suitable metric to use for this purpose. The main aim of this proposal is to develop a noise measurement/analysis strategy that can be used to estimate the hearing loss that will develop in workers exposed to complex industrial noise environments. Statistical learning models will be developed to achieve this goal.

描述（由申请人提供）：现有的噪声暴露损害风险标准完全基于能量度量。我们的动物模型实验表明，仅凭能量不足以描述复杂噪音的特征以达到听力保护的目的。这些数据表明，能源和统计指标峰度可能构成必要和足够的指标，预测听力损失，将导致长期的工业暴露。这一建议是这一努力的继续，但重点是人的状况。重要的是要用可比较的人类暴露和听力损失数据来验证动物结果。其主要目的是开发一种噪声测量/分析策略，可用于估计暴露于复杂工业噪声 * 环境的工人的听力损失。将开发统计学习模型来实现这一目标。模型开发需要获得以下数据：（1）暴露于高斯和非高斯噪声环境的高度筛选工人群体的稳定听力图。(2)记录、存档和分析每个受试者在整个工作班次过程中暴露的噪声波形。从该数据库开发的模型将识别在听力损失产生中重要的噪声变量，也可用于制定暴露标准。此外，为了回应从未受保护的工人收集数据的道德问题，将引入听力保护计划的要素。实验范例将生成所需的数据，用于开发经验基础，除了使用能量度量来预测暴露的危害之外，还使用量化噪声的时间和峰值特性的变量，例如峰度、冲击间隔和峰值直方图以及瞬态持续时间。数据将与北京大学的研究人员合作收集，包括在中国各种复杂的高噪音工业环境中雇用的至少1，250名工人。该噪声暴露和横截面测听数据将构成一个独特的数据库，该数据库将填补NIOSH承认的可用于制定听力保护暴露标准的数据空白。[* 在本提案中，我们使用的术语“复合噪声”是指非高斯噪声，在工作周期中，这种噪声可能是间歇性的、中断的和可变的。非高斯噪声在工业和军事中非常常见，其中它由具有嵌入的高电平瞬态（冲击或噪声突发）的背景高斯噪声组成。现有的噪声暴露安全标准完全基于能量度量。新的数据表明，单独使用能量并不是一个合适的指标。该提案的主要目的是开发一种噪声测量/分析策略，可用于估计暴露于复杂工业噪声环境的工人的听力损失。将开发统计学习模型来实现这一目标。