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Longitudinal Modeling of Glaucoma Progression Using 2-Dimensional Continuous-Time Hidden Markov Model

We propose a 2D continuous-time Hidden Markov Model (2D CT-HMM) for glaucoma progression modeling given longitudinal structural and functional measurements. CT-HMM is suitable for modeling longitudinal medical data consisting of visits at arbitrary times, and 2D state structure is more appropriate f...

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Bibliografische gegevens
Gepubliceerd in:Med Image Comput Comput Assist Interv
Hoofdauteurs: Liu, Yu-Ying, Ishikawa, Hiroshi, Chen, Mei, Wollstein, Gadi, Schuman, Joel S., Rehg, James M.
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: 2013
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Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC5988357/
https://ncbi.nlm.nih.gov/pubmed/24579171
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