Towards unified biomedical modeling with subgraph mining and factorization algorithms
Name
940768861-MIT.pdf
Description
Full printable version
Size
23.14 MB
Format
Adobe PDF
Checksum (MD5)
243c6e4aef448447ef6cdde77c1974f2
Author(s)
Luo, Yuan, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Peter Szolovits and Ozlem Uzuner.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
This dissertation applies subgraph mining and factorization algorithms to clinical narrative text, ICU physiologic time series and computational genomics. These algorithms aims to build clinical models that improve both prediction accuracy and interpretability, by exploring relational information in different biomedical data modalities including clinical narratives, physiologic time series and exonic mutations. This dissertation focuses on three concrete applications: implicating neurodevelopmentally coregulated exon clusters in phenotypes of Autism Spectrum Disorder (ASD), predicting mortality risk of ICU patients based on their physiologic measurement time series, and identifying subtypes of lymphoma patients based on pathology report text. In each application, we automatically extract relational information into a graph representation and collect important subgraphs that are of interest. Depending on the degree of structure in the data format, heavier machinery of factorization models becomes necessary to reliably group important subgraphs. We demonstrate that these methods lead to not only improved performance but also better interpretability in each application.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 157-181).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Persistent DSpace Link
