Disease progression modeling using Hidden Markov Models

By R. Sukkar, E. Katz, Yanwei Zhang, D. Raunig, B. Wyman. 2012

101 citations recorded by the source corpus.

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Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019, background): “Bayesian filtering [1] has been extensively used within the domain of time series prediction, with numerous applications across different fields – including target tracking [2], robotics [3], finance [4], and medicine [5].”
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (2015, neutral): “y studies that focus on modeling the temporal progression of a specic disease based on either intensive use of domain-specic knowledge [10,19,46] or taking advantage of advanced statistical methods [31,20,44,52]. Specically, studies have been conducted on Alzheimer’s disease [19,52,44], glaucoma [31”
Using recurrent neural network models for early detection of heart failure onset (2016, methodology): “…kidney disease to kidney failure using the Cox proportional hazard model, 36 the progression of Alzheimer’s disease using the hidden Markov model 37 and fused group Lasso, 38 the progression of glaucoma using using a 2-dimensional continuous-time hidden Markov model, 39 the progression of lung…”