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Data Driven Models in Time Series He...
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He, Rosemary Yuan,
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Data Driven Models in Time Series Health Records and Medical Imaging /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Data Driven Models in Time Series Health Records and Medical Imaging // Rosemary Yuan He.
Author:
He, Rosemary Yuan,
Description:
1 electronic resource (142 pages)
Notes:
Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:
Dissertations Abstracts International86-11B.
Subject:
Neurosciences. -
Online resource:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32041535
ISBN:
9798315742166
Data Driven Models in Time Series Health Records and Medical Imaging /
He, Rosemary Yuan,
Data Driven Models in Time Series Health Records and Medical Imaging /
Rosemary Yuan He. - 1 electronic resource (142 pages)
Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
With a large increase in the adoption of electronic health records, researchers have access to an unprecedented amount of health data on a patient-specific level, ranging from vitals every hour to medical images over a decade. Among these, time series data have been of particular interest to researchers as it is informative of disease progression, but difficult to model as its underlying distribution can be highly complex. On the other hand, machine learning models have been adopted across a wide range of tasks due to their ability to model non-linear relationships, rapid growth in computational power and decline in cost. However, machine learning applications in medical datasets face a set of unique challenges that decrease robustness and performance in off-the-shelf methods. As such, developing domain-specific algorithms to address these difficulties and improve model performance becomes crucial in successfully building and deploying these models in healthcare. In this dissertation, I describe my work in developing machine learning and generative AI methods for prediction, forecasting, and generative modeling specifically in the medical domain, using time series structured clinical data and longitudinal 3D medical images.
English
ISBN: 9798315742166Subjects--Topical Terms:
588700
Neurosciences.
Subjects--Index Terms:
Alzheimer's disease
Data Driven Models in Time Series Health Records and Medical Imaging /
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With a large increase in the adoption of electronic health records, researchers have access to an unprecedented amount of health data on a patient-specific level, ranging from vitals every hour to medical images over a decade. Among these, time series data have been of particular interest to researchers as it is informative of disease progression, but difficult to model as its underlying distribution can be highly complex. On the other hand, machine learning models have been adopted across a wide range of tasks due to their ability to model non-linear relationships, rapid growth in computational power and decline in cost. However, machine learning applications in medical datasets face a set of unique challenges that decrease robustness and performance in off-the-shelf methods. As such, developing domain-specific algorithms to address these difficulties and improve model performance becomes crucial in successfully building and deploying these models in healthcare. In this dissertation, I describe my work in developing machine learning and generative AI methods for prediction, forecasting, and generative modeling specifically in the medical domain, using time series structured clinical data and longitudinal 3D medical images.
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https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32041535
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