# Algorithmic Classification of Paraphasias

> **NIH NIH R01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2020 · $615,148

## Abstract

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.

## Key facts

- **NIH application ID:** 9989852
- **Project number:** 5R01DC015999-03
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Steven Bedrick
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $615,148
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989852

## Citation

> US National Institutes of Health, RePORTER application 9989852, Algorithmic Classification of Paraphasias (5R01DC015999-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9989852. Licensed CC0.

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