Algorithmic Classification of Paraphasias

总结 失范是指在语言产生过程中无法获取和提取预期词汇的现象，是一种主要的语言失范现象。 这是一种获得性神经性语言障碍的特征，称为失语症。失语症大约影响1 在美国，失语症的发病率将增加1000万人，并且考虑到人口的老龄化趋势， 未来几十年。沟通困难对与健康有关的生活质量有重大影响， 失语症（PWA）患者，并与大量的医疗费用。的当前方法 诊断和表征命名障碍涉及对抗命名测试（CNT），其中受试者被 给他们一张图片，并要求他们说出图片的内容。例如，他们可能会看到一幅 一个听诊器，并且被期望说这个词，“听诊器”。一个语义失范的主体， 然而，可能改为说“救护车”--这个词虽然不正确，但在语义上与目标相关 Word.相比之下，一个有不同类型失范的主体可能会说“望远镜”--一个语义上无关的词。 一个词，但一个是语音相关的。通过提出几个这样的项目，并计算数量， 通过分析受试者产生的错误类型，临床医生可以了解受试者所产生的失范的类型和严重性。 受试者正在经历 CNT虽然在临床上有价值，但存在几个问题。它们是耗时的管理，并 为了给它们评分，临床医生必须做出大量知情但主观的决定。本课题 我们将开发一个计算机化的系统，使这些决定自动化，这在两个方面是有用的。第一、 它将使临床医生更容易和更快地管理这些测试，这将节省时间， 临床医生关注他们的病人而不是评分测试。第二，我们的自动化方法将开放 许多新的方式，对抗命名测试可以使用的大门，因为他们将不再需要 专家临床医生来管理它们。作为这方面的一个例子，在本项目的第二和第三个目标中，我们将 将我们的计算机化评分系统扩展到CNT上下文之外，并扩展到自然语言。我们将 开发算法来识别口语样本中的失语症， 分类，因为他们的类型，因为我们可以对CNT测试项目。这将使临床医生能够可靠地， 客观分析患者的言语，筛查和评估患者的命名障碍程度。

Summary Anomia is the inability to access and retrieve the intended words during language production, and is a cardinal feature of the acquired neurogenic language disorder known as aphasia. Aphasia affects approximately 1 million people in the US and, given the aging trend in the population, the incidence of aphasia will increase in the coming decades. Communication difficulties have a significant impact on the health-related quality of life of people with aphasia (PWA), and are associated with substantial healthcare costs. Current methods for diagnosing and characterizing anomia involve confrontation naming tests (CNTs), in which a subject is presented with an image and asked to verbally identify its contents. For example, they might see a drawing of a stethoscope, and would be expected to say the word, “stethoscope.” A subject with semantic anomia, however, might instead say “ambulance”— a word that, while incorrect, is semantically related to the target word. A subject with a different kind of anomia, in contrast, might say “telescope”— a semantically unrelated word, but one that is phonologically related. By presenting several such items, and counting the number and types of errors produced by the subject, a clinician can learn about the type and severity of anomia that the subject is experiencing. CNTs, while clinically valuable, have several problems. They are time-consuming to administer, and to score them, the clinician must make a large number of informed, but subjective, decisions. In this project, we will be developing a computerized system to automate these decisions, which will be useful in two ways. First, it will make it much easier and faster for clinicians to administer these tests, which will save time, and will allow the clinicians to focus on their patients rather than on scoring tests. Second, our automated approach will open the door to many new ways that confrontation naming tests can be used, since they will no longer require an expert clinician to administer them. As one example of this, in the second and third aims of this project, we will be extending our computerized scoring system beyond the CNT context, and into natural language. We will develop algorithms to recognize paraphasias in spoken language samples, and to make the same classifications as to their type as we can make on CNT test items. This will enable clinicians to reliably and objectively analyze their patients' speech, and to screen for and assess their level of anomia.