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AI and Big Data in Health : = Boosting Reliability and Efficiency in Predictive Healthcare Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	AI and Big Data in Health :/
Reminder of title:	Boosting Reliability and Efficiency in Predictive Healthcare Models.
Author:	Wang, Yuqing.
Description:	1 online resource (142 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798380154550

AI and Big Data in Health : = Boosting Reliability and Efficiency in Predictive Healthcare Models.
Wang, Yuqing.

AI and Big Data in Health :Boosting Reliability and Efficiency in Predictive Healthcare Models. - 1 online resource (142 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of California, Santa Barbara, 2023.

Includes bibliographical references

In the era of data-driven decision-making, healthcare stands as a critical domain where machine learning (ML) techniques can bring transformative changes. However, the application of ML in healthcare faces unique challenges due to clinicians' limited understanding of intricate ML processes, the diverse and unstructured nature of healthcare data, high computational costs, and the "black box" problem associated with ML algorithms. The recent advent of large language models (LLMs) further introduces the challenge of developing appropriate prompts to guide these models to provide meaningful and contextually relevant responses.This dissertation grapples with these challenges across a series of studies. First, we analyze multiple ML configurations for the prediction of multiple organ failure in trauma patients, highlighting the impact of classifier choice on performance. Next, we propose a multimodal Transformer model for early sepsis prediction, demonstrating its efficacy over competitive baselines. To address the computational costs, we propose an efficient model for multivariate time series classification. Reinforcement learning is then applied to predict the need for blood transfusion in intensive care units, offering a decision support tool for effective treatment recommendations. Lastly, we conduct a comparative study on the readiness of LLMs for healthcare, introducing a novel prompting strategy to maximize their effectiveness.The primary objective of this dissertation is to facilitate the advancement, comprehensive evaluation, and systematic optimization of machine learning applications specifically in the healthcare domain. Our work aims to connect complex ML methodologies with practical healthcare applications. As our work progresses, we remain committed to the continuous refinement and enhancement of these models. Our approach aims to balance technical sophistication with ease of use, minimizing the trade-off between the two. We believe that our ML advancements, tailored to the unique needs of healthcare applications, can improve patient outcomes and streamline healthcare delivery.

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

Mode of access: World Wide Web

ISBN: 9798380154550Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Decision-makingIndex Terms--Genre/Form:

554714
Electronic books.

AI and Big Data in Health : = Boosting Reliability and Efficiency in Predictive Healthcare Models.
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