Algorithmic Classification of Paraphasias

失语症的算法分类

• 批准号: 10256805
• 负责人: Steven Bedrick
• 金额: $ 60.61万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10256805
• 关键词: Affect; Aging; Agreement; Algorithms; Ambulances; Anomia; Aphasia; Area; Brain Injuries; Classification; Clinical; Cognitive; Communication; Computational algorithm; Computer Assisted; Computers; Consumption; Cues; Detection; Diagnosis; Engineering; Frequencies; Goals; Health Care Costs; Human; Hybrids; Image; Impairment; Incidence; Individual; Intervention; Judgment; Knowledge; Language; Language Disorders; Learning; Linguistics; Manuals; Measures; Methods; Modeling; Names; Patients; Performance; Population; Process; Production; Provider; Publishing; Research Personnel; Sampling; Scientist; Semantics; Sensitivity and Specificity; Severities; Source; Speech; Statistical Data Interpretation; Stethoscopes; Stroke; Structure; System; Testing; Time; Work; Workload; classification algorithm; clinically relevant; computerized; crowdsourcing; experience; health related quality of life; lexical; natural language; novel; phonology; profiles in patients; recurrent neural network; trend

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.

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