Multistate Markov models for disease progression with classification error

By C. Jackson, L. Sharples, S. Thompson, S. Duffy, E. Couto. 2003

341 citations recorded by the source corpus.

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Allelomimetic synchronization in Merino sheep (2007, neutral): “Collective change rates L(A,I ) and M(A,I ) were estimated from S(t), using the package msm (Jackson et al. 2003) and the statistical software R (R Development Core Team 2005).”
Learning (predictive) risk scores in the presence of censoring due to interventions (2015, background): “For example, Jackson et al (2003) study abdominal aortic aneurysms in elderly men.”
$\mathtt {Deepr}$: A Convolutional Net for Medical Records (2016, neutral): “Capturing disease progression has been of great interest [19], [20], and much effort has been spent on Markov models [5], [18], [21].”
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine (2016, background): “Capturing disease progression has been of great interest [27, 28], and much effort has been spent on Markov models [7, 29] and dynamic Bayesian networks [30].”
Multi-State Models for Panel Data: The msm Package for R (2011, background): “Misclassification models An important special case of HMMs is the multi-state model with misclassification, where the observed data are states, assumed to be misclassifications of the true, underlying states (Jackson et al. 2003).”