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Analytical Methods for Diagnosis and...

Davis, Tyler Austin.

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Analytical Methods for Diagnosis and Prediction of Health Conditions.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analytical Methods for Diagnosis and Prediction of Health Conditions./
Author:	Davis, Tyler Austin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	171 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
Contained By:	Dissertations Abstracts International84-09B.
Subject:	Medicine. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30313940
ISBN:	9798377643937

Analytical Methods for Diagnosis and Prediction of Health Conditions.
Davis, Tyler Austin.

Analytical Methods for Diagnosis and Prediction of Health Conditions. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 171 p.

Source: Dissertations Abstracts International, Volume: 84-09, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2023.

.

Recent years have seen a tremendous amount of growth in the performance and adoption of artificial intelligence (AI) and machine learning (ML) systems. These systems now permeate our lives, underpinning everything from web search to credit card fraud detection and photography. In principle, these advancements could also be applied to the domain of healthcare, where they could improve patient outcomes.However, despite the almost fifty years that have elapsed since the first National Institutes of Health AI in Medicine (AIM) workshop in 1973 and the ubiquity of AI systems in our daily lives, AIM has not yet lived up to its lofty promises. AIM systems have seen limited deployment due to challenges including data missingness, data heterogeneity, explainability, and generalizability across variances in patient populations. The recent increase in the availability of electronic health record information, the variety and cost-effectiveness of mobile sensors, and the capabilities of machine learning algorithms promise to help improve healthcare delivery if challenges can be overcome. Through techniques such as interpretable analysis of heterogeneous information networks and missingness-aware modeling, we demonstrate that the challenges of AI in Medicine can be overcome in order to improve healthcare access, aid physicians, and generate new insights into disease.

ISBN: 9798377643937Subjects--Topical Terms:

641104
Medicine.
Subjects--Index Terms:

AI systems

Analytical Methods for Diagnosis and Prediction of Health Conditions.
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