While there exist numerous methods to identify binary phenotypes (ie COPD) using
electronic health record (EHR) data, few exist to ascertain the timings of phenotype events …

Abstract Hidden Markov models (HMMs) are widely used to analyze heterogeneous
longitudinal data owing to their capability to model dynamic heterogeneity. Early …

Abstract Hidden Markov models (HMMs) describe the relationship between two stochastic
processes, namely, an observed outcome process and an unobservable finite-state …

In psychological, social, behavioral, and medical studies, hidden Markov models (HMMs)
have been extensively applied to the simultaneous modeling of longitudinal observations …

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are
valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden …

Objective While there exist numerous methods to predict binary phenotypes using electronic
health record (EHR) data, few exist for prediction of phenotype event times, or equivalently …

Under the Bayesian framework, this study proposes a Tweedie compound Poisson partial
linear mixed model on the basis of Bayesian P-spline approximation to nonparametric …

Nonlinear mixed‐effects (NLME) models are commonly used in longitudinal studies such as
pharmacokinetics and HIV viral dynamics studies. NLME models are often derived based on …

The application of Hidden Markov Models (HMMs) in the study of genetic and neurological
disorders has shown significant potential in advancing our understanding and treatment of …

Electronic health records (EHRs) promise unprecedented opportunities for in silico clinical
and translational discovery ranging from disease risk prediction to survival analysis …