title: Using Hidden Markov Models to Determine Changes in Subject Data over Time, Studying the Immunoregulatory effect of Mesenchymal Stem Cells.
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, anunsupervised learningdata 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 construted 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 labeled 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.
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Edgar F
authors.initials: EF
authors.lastname: Black
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Luigi
authors.initials: L
authors.lastname: Marini
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Ashwini
authors.initials: A
authors.lastname: Vaidya
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Dora
authors.initials: D
authors.lastname: Berman
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Melissa
authors.initials: M
authors.lastname: Willman
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Dan
authors.initials: D
authors.lastname: Salomon
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Amelia
authors.initials: A
authors.lastname: Bartholomew
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Norma
authors.initials: N
authors.lastname: Kenyon
authors.affiliation: National Center for Supercomputing Applications University of Illinois at Urbana-Champaign.
authors.forename: Kenton
authors.initials: K
authors.lastname: McHenry
keywords: Hidden Markov Models; data mining; unsupervised learning