Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/209007
Title: MULTI-VIEW LEARNING USING DEPENDENCY MODELS FOR MEDICAL DECISION MAKING
Authors: PARVATHY SUDHIR PILLAI
Keywords: Multi-view learning, Probabilistic graphical models, Dependency learning, Bayesian inference, Alzheimer's disease
Issue Date: 19-Jul-2019
Citation: PARVATHY SUDHIR PILLAI (2019-07-19). MULTI-VIEW LEARNING USING DEPENDENCY MODELS FOR MEDICAL DECISION MAKING. ScholarBank@NUS Repository.
Abstract: Data fusion involves the integration of multiple sources of data, possibly collected in different modalities, that describe the same entity. Groups of features extracted from each data source present distinct views of information about the entity. Though the views may differ, combining the complementary information provides a fuller picture and better insights in analytic tasks. Moreover, there are statistical dependencies between the views as they originate from the same entity. This thesis proposes different methods to combine multi-view medical data to solve various tasks in dementia management. We develop supervised machine learning models for disease identification, staging, and modeling progression of dementia that work on the total information from distinct data sources, rather than using them separately. Our main goal is to build a probabilistic graphical model-based framework that understands the correlative, causative, and complementary nature of the features across views constructed from the same or separate modalities.
URI: https://scholarbank.nus.edu.sg/handle/10635/209007
Appears in Collections:Ph.D Theses (Open)

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