论文标题
贝叶斯主动差异选择加速心理测量测试
Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection
论文作者
论文摘要
心理测量功能估计的经典方法要么需要过度测量，要么仅产生目标心理测量功能的低分辨率近似。在本文中，我们提出了一种新的解决方案，用于快速筛选给定患者的心理测量功能估计。我们使用贝叶斯主动模型选择执行自动化的纯音听力图测试，目的是快速查找当前的听力图是否与以前的听觉图不同。我们使用美国国家职业安全与健康研究所NIOSH的听力数据来验证我们的方法。最初的结果表明，有了几种调子，我们可以检测到患者的听力学功能是否在两个测试会话之间置于高度置信度之间。
Classical methods for psychometric function estimation either require excessive measurements or produce only a low-resolution approximation of the target psychometric function. In this paper, we propose a novel solution for rapid screening for a change in the psychometric function estimation of a given patient. We use Bayesian active model selection to perform an automated pure-tone audiogram test with the goal of quickly finding if the current audiogram will be different from a previous audiogram. We validate our approach using audiometric data from the National Institute for Occupational Safety and Health NIOSH. Initial results show that with a few tones we can detect if the patient's audiometric function has changed between the two test sessions with high confidence.
