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Simulating Future Actions in Speech ...

Kaur, Antarpreet,

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Simulating Future Actions in Speech Therapy Sessions: Development of a Dataset and Predictive Model /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Simulating Future Actions in Speech Therapy Sessions: Development of a Dataset and Predictive Model // Antarpreet Kaur.
Author:	Kaur, Antarpreet,
Description:	1 electronic resource (47 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Speech therapy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31299616
ISBN:	9798382831800

Simulating Future Actions in Speech Therapy Sessions: Development of a Dataset and Predictive Model /
Kaur, Antarpreet,

Simulating Future Actions in Speech Therapy Sessions: Development of a Dataset and Predictive Model /Antarpreet Kaur. - 1 electronic resource (47 pages)

Source: Masters Abstracts International, Volume: 85-12.

The increasing demand for speech-language pathologists (SLPs) against their limited availability poses significant challenges in delivering effective pediatric speech therapy. In response, the National AI Institute for Exceptional Education has launched an initiative to enhance the efficiency of speech therapy services through an AI-driven framework that includes components like the AI Orchestrator, AI Screener, and a Simulator to automate and enhance therapeutic decision-making.This thesis focuses on developing a simulator that uses a limited dataset derived from SOAP (Subjective, Objective, Assessment, Plan) notes, which are rich in narrative but lack quantifiable metrics. This necessitated innovative processing to convert these descriptions into structured data suitable for AI analysis, using the BART (Bidirectional and Auto- Regressive Transformers) model. Known for its effectiveness in handling complex textual data, BART is ideal for simulating potential future therapy sessions based on past notes.A conceptual human-in-the-loop system is proposed to refine the AI's outputs with ex- pert SLP input, enhancing its practical utility and providing predictive insights that support more personalized therapy interventions.This thesis demonstrates a novel approach to leveraging AI in healthcare, showcasing the potential of data-driven technologies to transform therapeutic practices and laying groundwork for future research in AI-enhanced speech therapy.

English

ISBN: 9798382831800Subjects--Topical Terms:

520446
Speech therapy.
Subjects--Index Terms:

Speech-language pathologists

Simulating Future Actions in Speech Therapy Sessions: Development of a Dataset and Predictive Model /
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520 $a The increasing demand for speech-language pathologists (SLPs) against their limited availability poses significant challenges in delivering effective pediatric speech therapy. In response, the National AI Institute for Exceptional Education has launched an initiative to enhance the efficiency of speech therapy services through an AI-driven framework that includes components like the AI Orchestrator, AI Screener, and a Simulator to automate and enhance therapeutic decision-making.This thesis focuses on developing a simulator that uses a limited dataset derived from SOAP (Subjective, Objective, Assessment, Plan) notes, which are rich in narrative but lack quantifiable metrics. This necessitated innovative processing to convert these descriptions into structured data suitable for AI analysis, using the BART (Bidirectional and Auto- Regressive Transformers) model. Known for its effectiveness in handling complex textual data, BART is ideal for simulating potential future therapy sessions based on past notes.A conceptual human-in-the-loop system is proposed to refine the AI's outputs with ex- pert SLP input, enhancing its practical utility and providing predictive insights that support more personalized therapy interventions.This thesis demonstrates a novel approach to leveraging AI in healthcare, showcasing the potential of data-driven technologies to transform therapeutic practices and laying groundwork for future research in AI-enhanced speech therapy.
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