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Building Computational Representations of Medical Guidelines Using Large Language Models and Transfer Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Building Computational Representations of Medical Guidelines Using Large Language Models and Transfer Learning./
Author:	Gopalakrishnan, Seethalakshmi.
Description:	1 online resource (140 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
Contained By:	Dissertations Abstracts International85-06A.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798381090901

Building Computational Representations of Medical Guidelines Using Large Language Models and Transfer Learning.
Gopalakrishnan, Seethalakshmi.

Building Computational Representations of Medical Guidelines Using Large Language Models and Transfer Learning. - 1 online resource (140 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: A.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2023.

Includes bibliographical references

This dissertation explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical Practice Guidelines. The outcomes of causality extraction from Clinical Practice Guidelines on gestational diabetes are presented, marking a first in the field. We also release, the first of its kind, an annotated corpus of causal statements in the Clinical Practice Guidelines.We address the challenge of classifying causal sentences with a small amount of annotated data at the inter-sentence level by treating it as a cross-domain transfer learning problem. Obtaining these classified sentences is the first step in extracting causality. Furthermore, we delve into the importance of modal verbs and the degree of influence from cause to effect. We show the capability of three models (BERT, DistilBERT, and BioBERT) to identify the degree of influence in the text.Lastly, we tackle the challenge of sparse annotated data for the causality extraction from Clinical Practice Guidelines by, again, using transfer learning. We investigate the correlation between data similarity and the efficacy of transfer learning. We also investigate a zero-shot and few-shot approach to cross-domain transfer learning, and quantify the link between data similarity and success rates. With the cross-domain few-shot transfer learning, we achieve an F1-score of 0.81, which suggests transfer learning as a possible solution to address the limited availability of annotated data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381090901Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Causality extractionIndex Terms--Genre/Form:

554714
Electronic books.

Building Computational Representations of Medical Guidelines Using Large Language Models and Transfer Learning.
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653 $a Natural language processing
653 $a Transfer learning
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