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 for glaucoma since the time courses of functional and structural degeneration are usually different. The learned model not only corroborates the clinical findings that structural degeneration is more evident than functional degeneration in early glaucoma and the opposite is observed in more advanced stages, but also reveals the exact stages where the trend reverses. A method to detect time segments of fast progression is also proposed. Our results show that this detector can effectively identify patients with rapid degeneration. The model and the derived detector can be of clinical value for glaucoma monitoring.