Title:
Unsupervised learning of disease subtypes from continuous time Hidden Markov Models of disease progression
Unsupervised learning of disease subtypes from continuous time Hidden Markov Models of disease progression
| dc.contributor.advisor | Rehg, James M. | |
| dc.contributor.author | Gupta, Amrita | |
| dc.contributor.committeeMember | Sun, Jimeng | |
| dc.contributor.committeeMember | Hutman, Theodore | |
| dc.contributor.department | Computational Science and Engineering | |
| dc.date.accessioned | 2016-01-07T17:24:24Z | |
| dc.date.available | 2016-01-07T17:24:24Z | |
| dc.date.created | 2015-12 | |
| dc.date.issued | 2015-08-21 | |
| dc.date.submitted | December 2015 | |
| dc.date.updated | 2016-01-07T17:24:24Z | |
| dc.description.abstract | The detection of subtypes of complex diseases has important implications for diagnosis and treatment. Numerous prior studies have used data-driven approaches to identify clusters of similar patients, but it is not yet clear how to best specify what constitutes a clinically meaningful phenotype. This study explored disease subtyping on the basis of temporal development patterns. In particular, we attempted to differentiate infants with autism spectrum disorder into more fine-grained classes with distinctive patterns of early skill development. We modeled the progression of autism explicitly using a continuous-time hidden Markov model. Subsequently, we compared subjects on the basis of their trajectories through the model state space. Two approaches to subtyping were utilized, one based on time-series clustering with a custom distance function and one based on tensor factorization. A web application was also developed to facilitate the visual exploration of our results. Results suggested the presence of 3 developmental subgroups in the ASD outcome group. The two subtyping approaches are contrasted and possible future directions for research are discussed. | |
| dc.description.degree | M.S. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/54364 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Unsupervised learning | |
| dc.subject | Disease progression model | |
| dc.title | Unsupervised learning of disease subtypes from continuous time Hidden Markov Models of disease progression | |
| dc.type | Text | |
| dc.type.genre | Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Rehg, James M. | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | School of Computational Science and Engineering | |
| relation.isAdvisorOfPublication | af5b46ec-ffe2-4ce4-8722-1373c9b74a37 | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isOrgUnitOfPublication | 01ab2ef1-c6da-49c9-be98-fbd1d840d2b1 | |
| thesis.degree.level | Masters |