Using hidden Markov models to determine changes in subject data over time, studying the immunoregulatory effect of mesenchymal stem cells

Edgar F. Black, Luigi Marini, Ashwini Vaidya, Dora Berman, Melissa Willman, Dan Salomon, Amelia Bartholomew, Norma Kenyon, Kenton McHenry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

A novel application of Hidden Markov Models is used to help research intended to test the immunuregulatory effects of mesenchymal stem cells in a cynomolgus monkey model of islet transplantation. The Hidden Markov Model, an unsupervised learning data mining technique, is used to automatically determine the postoperative day (POD) corresponding to a decrease of graft function, a possible sign of transplant rejection, on nonhuman primates after isolated islet cell transplant. Currently, decrease of graft function is being determined solely on experts' judgment. Further, information gathered from the evaluation of constructed Hidden Markov Models is used as part of a clustering method to aggregate the nonhuman subjects into groups or clusters with the objective of finding similarities that could potentially help predict the health outcome of subjects undergoing postoperative care. Results on expert labelled data show the HMM to be accurate 60% of the time. Clusters based on the HMMs further suggest a possible correspondence between donor haplotypes matching and loss of function outcomes.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE 10th International Conference on eScience, eScience 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages83-91
Number of pages9
ISBN (Electronic)9781479942886
DOIs
StatePublished - Dec 2 2014
Externally publishedYes
Event10th IEEE International Conference on eScience, eScience 2014 - Guaruja, Brazil
Duration: Oct 20 2014Oct 24 2014

Publication series

NameProceedings - 2014 IEEE 10th International Conference on eScience, eScience 2014
Volume1

Other

Other10th IEEE International Conference on eScience, eScience 2014
CountryBrazil
CityGuaruja
Period10/20/1410/24/14

Keywords

  • Hidden Markov Models
  • data mining
  • unsupervised learning

ASJC Scopus subject areas

  • Computer Science(all)

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