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Novel Statistical Learning Methods for Multi-Modality Heterogeneous Data Fusion in Health Care Applications

Abstract With the development of computer and sensing technology, rich datasets have become available in many fields such as health care, manufacturing, transportation, just to name a few. Also, data come from multiple heterogeneous sources or modalities. This is a common phenomenon in health care systems. While multi-modality data fusion is a promising research area, there are several special challenges in health care applications. (1) The integration of biological and statistical model is a big challenge; (2) It is commonplace that data from various modalities is not available for every patient due to cost, accessibility, and other reasons. This results in a special missing data structure in which different modalities may be missed in “blocks”. Th... (more)
Created Date 2019
Contributor Liu, Xiaonan (Author) / Li, Jing (Advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Fatyga, Mirek (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering
Type Doctoral Dissertation
Extent 110 pages
Language English
Note Doctoral Dissertation Industrial Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis