Show simple item record

dc.contributor.advisorWang, Hua
dc.contributor.authorBrand, Lodewijk Willem Cornelis
dc.contributor.committeememberZhang, Hao
dc.contributor.committeememberMehta, Dinesh P.
dc.contributor.committeememberKlein-Seetharaman, Judith
dc.contributor.committeememberCrosby, Ralph
dc.date.accessioned2021-09-13T10:22:17Z
dc.date.available2021-09-13T10:22:17Z
dc.date.issued2021
dc.descriptionIncludes bibliographical references.
dc.description2021 Summer.
dc.description.abstractAlzheimer's Disease (AD) is a serious public health issue that results in significant social and financial burdens on the individuals and communities impacted. In order to tackle this public health crisis it is critical that the clinical and computational research communities collaborate to identify possible causes of this progressive memory disease. Close collaboration between these two communities has the potential to result in promising therapeutic treatments for AD and other health conditions. This dissertation presents a collection of algorithms and associated derivations designed to predict the progression of AD using multi-task and structured regularization techniques, clustering membership by way of nonnegative matrix factorization, and COVID-19 clinical outcome prediction using multi-instance learning methods. This work presents novel algorithms for handling multimodal and longitudinal data and details approaches for multitask and multi-instance learning techniques that can be applied in other fields. Extensive discussions on algorithm predictive performance, interpretability, and implementation are provided for each method and are designed to serve as a framework for future research.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierBrand_mines_0052E_12237.pdf
dc.identifierT 9195
dc.identifier.urihttps://hdl.handle.net/11124/176532
dc.languageEnglish
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2021 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectCOVID-19
dc.subjectmulti-instance learning
dc.subjectmultimodal data
dc.subjectmatrix factorization
dc.subjectAlzheimer's disease
dc.subjectmulti-task learning
dc.titleDesign, implementation and interpretation of algorithms to predict the progression of Alzheimer's disease
dc.typeText
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado School of Mines
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record