Material Type  

 학위논문

 

0017161367

Date and Time of Latest Transaction  

20250211151347

ISBN  

9798382813813

DDC  

574

Author  

Miao, Brenda.

Title/Author  

Uncovering Strategies for Personalized Treatment Selection Using Large Language Models.

Publish Info  

[S.l.] : University of California, San Francisco., 2024

Publish Info  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Material Info  

168 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

General Note  

Includes supplementary digital materials.

General Note  

Advisor: Butte, Atul.

학위논문주기  

Thesis (Ph.D.)--University of California, San Francisco, 2024.

Abstracts/Etc  

요약Healthcare data has never been so accessible to patients and physicians, from smartphones and other remote monitoring devices to improved access for patients to their own Electronic Medical Record (EMR) history and clinical notes. Despite the ubiquity of healthcare data collection and distribution, there remains a significant gap in understanding the impacts of this data on clinical care. Insights from these digital health tools and downstream clinical decision-making processes are often only captured in medical notes, which are complex, sparse, unstructured, and difficult to model even with traditional deep learning methods. Only recently have large language models (LLMs) emerged that are capable of zero- or few-shot clinical language, without the need for large, manually annotated datasets. In this dissertation, I develop methods to adapt LLMs to healthcare tasks, particularly for identifying points of actionable insights for both digital and pharmaceutical therapeutics. These approaches demonstrate the ways in which digital health products can impact clinical care, as well as provide methods to identify reasons for medication class switching that consider the complexities of patient care beyond lab values and diagnosis codes. While careful, rigorous research is needed to ensure that these approaches are effective in facilitating patient care improvements and to reduce any potential for harm, the rapid pace of language model development provides an extraordinary opportunity to transform clinical practice. These new methods allow us to take an unprecedented look at the conversations, decisions, and medical expertise captured in billions of clinical notes and other clinical text, and to learn from this shared knowledge to accelerate medical research, improve clinical guidelines, and personalize patient care. 

Subject Added Entry-Topical Term  

Bioinformatics.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Language.

Index Term-Uncontrolled  

Clinical text processing

Index Term-Uncontrolled  

Large language models

Index Term-Uncontrolled  

Treatment strategy optimization

Index Term-Uncontrolled  

Electronic Medical Record history

Index Term-Uncontrolled  

Deep learning methods

Added Entry-Corporate Name  

University of California, San Francisco Biological and Medical Informatics

Host Item Entry  

Dissertations Abstracts International. 85-12A.

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:658655