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Mixture modeling for identifying subtypes in disease course mapping
- Source :
- Information Processing for Medical Imaging, Aasa Feragen; Stefan Sommer; Julia Schnabel; Mads Nielsen. Information Processing for Medical Imaging, Springer, pp.571-582, 2021, ⟨10.1007/978-3-030-78191-0_44⟩, Information Processing in Medical Imaging, 27th International Conference, Information Processing in Medical Imaging, 27th International Conference, Jun 2021, Virtual event, France. ⟨10.1007/978-3-030-78191-0_44⟩, Lecture Notes in Computer Science ISBN: 9783030781903, IPMI
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- International audience; Disease modeling techniques summarize the possible trajectories of progression from multimodal and longitudinal data. These techniques often assume that individuals form a homogeneous cluster, thus ignoring possible disease subtypes within the population. We extend a non-linear mixed-effect model used for disease course mapping with a mixture framework. We jointly estimate model parameters and subtypes with a tempered version of a stochastic approximation of the Expectation Maximisation algorithm. We show that our model recovers the ground truth parameters from synthetic data, in contrast to the naive solution consisting in post hoc clustering of individual parameters from a one-class model. Applications to Alzheimer's disease data allows the unsupervised identification of disease subtypes associated with distinct relationship between cognitive decline and progression of imaging and biological biomarkers.
- Subjects :
- Computer science
Population
Stochastic approximation
Machine learning
computer.software_genre
01 natural sciences
Synthetic data
010104 statistics & probability
03 medical and health sciences
0302 clinical medicine
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
Alzheimer's disease subtypes
mixed-effect models
0101 mathematics
Cognitive decline
mixture models
education
Cluster analysis
Non-linear mixed-effect model
ComputingMilieux_MISCELLANEOUS
Mixture model
Ground truth
education.field_of_study
MCMC-SAEM
business.industry
Contrast (statistics)
Disease course mapping
Disease progression modelling
[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]
Artificial intelligence
business
computer
[STAT.ME]Statistics [stat]/Methodology [stat.ME]
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-030-78190-3
- ISBNs :
- 9783030781903
- Database :
- OpenAIRE
- Journal :
- Information Processing for Medical Imaging, Aasa Feragen; Stefan Sommer; Julia Schnabel; Mads Nielsen. Information Processing for Medical Imaging, Springer, pp.571-582, 2021, ⟨10.1007/978-3-030-78191-0_44⟩, Information Processing in Medical Imaging, 27th International Conference, Information Processing in Medical Imaging, 27th International Conference, Jun 2021, Virtual event, France. ⟨10.1007/978-3-030-78191-0_44⟩, Lecture Notes in Computer Science ISBN: 9783030781903, IPMI
- Accession number :
- edsair.doi.dedup.....a81bacea9cc6fab79c992abeeceefec0