Disentangling disease heterogeneity with max-margin multiple hyperplane classifier

Erdem Varol, Aristeidis Sotiras, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

There is ample evidence for the heterogeneous nature of diseases. For example, Alzheimer’s Disease, Schizophrenia and Autism Spectrum Disorder are typical disease examples that are characterized by high clinical heterogeneity, and likely by heterogeneity in the underlying brain phenotypes. Parsing this heterogeneity as captured by neuroimaging studies is important both for better understanding of disease mechanisms, and for building subtype-specific classifiers. However, few existing methodologies tackle this problem in a principled machine learning framework. In this work, we developed a novel non-linear learning algorithm for integrated binary classification and subpopulation clustering. Non-linearity is introduced through the use of multiple linear hyperplanes that form a convex polytope that separates healthy controls from pathologic samples. Disease heterogeneity is disentangled by implicitly clustering pathologic samples through their association to single linear sub-classifiers. We show results of the proposed approach from an imaging study of Alzheimer’s Disease, which highlight the potential of the proposed approach to map disease heterogeneity in neuroimaging studies.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages702-709
Number of pages8
DOIs
StatePublished - Oct 1 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9349
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Disentangling disease heterogeneity with max-margin multiple hyperplane classifier'. Together they form a unique fingerprint.

Cite this