國立虎尾科技大學 |

Generalizable Risk Predictive Deep Learning Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Generalizable Risk Predictive Deep Learning Models./
Author:	Amrollahi, Fatemeh.
Description:	1 online resource (109 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: A.
Contained By:	Dissertations Abstracts International85-07A.
Subject:	Bioinformatics. -
Online resource:	click for full text (PQDT)
ISBN:	9798381426465

Generalizable Risk Predictive Deep Learning Models.
Amrollahi, Fatemeh.

Generalizable Risk Predictive Deep Learning Models. - 1 online resource (109 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: A.

Thesis (Ph.D.)--University of California, San Diego, 2023.

Includes bibliographical references

The broad adoption of Electronic Health Records (EHRs) accelerated the development and usage of Machine learning (ML) and Deep learning (DL) algorithms in clinical settings. The potential uses of ML and DL algorithms to augment clinical decision-making in domains such as forecasting disease onset and progression, predicting response to treatments, and optimization of treatment protocols are growing. While most existing ML/DL models are trained on single-centered data, multi-center datasets are becoming increasingly available. However, curation of such datasets is often time-consuming and lags behind shifts in disease prevalence and changes in workflow practices, which are known to cause data distribution shifts and degradation in ML/DL model performance.In addition, data privacy concerns and patient confidentiality regulations continue to pose a major barrier to multicenter EHR data access. In this work, we developed algorithms to enable DL models to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. We validated and compared our methods in the context of early prediction of sepsis using data across four geographically distinct healthcare systems. We explore several methods to enhance the generalizability of DL models. We focus on three areas: Continual Learning, Federated Learning, and Generative Adversarial Networks (GANs), introducing new algorithms within each area and comparing their performance against state-of-the-art models. We have validated and compared these methods in one of the most challenging tasks for biomedical researchers: predicting the onset of sepsis in intensive care units.

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

Mode of access: World Wide Web

ISBN: 9798381426465Subjects--Topical Terms:

583857
Bioinformatics.
Subjects--Index Terms:

Continual LearningIndex Terms--Genre/Form:

554714
Electronic books.

Generalizable Risk Predictive Deep Learning Models.
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500 $a Advisor: Nemati, Shamim.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2023.
504 $a Includes bibliographical references
520 $a The broad adoption of Electronic Health Records (EHRs) accelerated the development and usage of Machine learning (ML) and Deep learning (DL) algorithms in clinical settings. The potential uses of ML and DL algorithms to augment clinical decision-making in domains such as forecasting disease onset and progression, predicting response to treatments, and optimization of treatment protocols are growing. While most existing ML/DL models are trained on single-centered data, multi-center datasets are becoming increasingly available. However, curation of such datasets is often time-consuming and lags behind shifts in disease prevalence and changes in workflow practices, which are known to cause data distribution shifts and degradation in ML/DL model performance.In addition, data privacy concerns and patient confidentiality regulations continue to pose a major barrier to multicenter EHR data access. In this work, we developed algorithms to enable DL models to transfer their knowledge across institutional boundaries and learn from new episodes of patient care without forgetting previously learned patterns. We validated and compared our methods in the context of early prediction of sepsis using data across four geographically distinct healthcare systems. We explore several methods to enhance the generalizability of DL models. We focus on three areas: Continual Learning, Federated Learning, and Generative Adversarial Networks (GANs), introducing new algorithms within each area and comparing their performance against state-of-the-art models. We have validated and compared these methods in one of the most challenging tasks for biomedical researchers: predicting the onset of sepsis in intensive care units.
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653 $a Continual Learning
653 $a Federated Learning
653 $a Generative Adversarial Networks
653 $a Deep learning
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