9 results found
Luo Y, Stephens D, Buckeridge DL, 2021, Bayesian clustering for continuous-time hidden Markov models, CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, ISSN: 0319-5724
We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized linear observation model. Specifically in this paper, we carry out finite and infinite mixture model-based clustering for a CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). For a finite mixture model with a prior on the number of components, we implement reversible-jump MCMC to facilitate the trans-dimensional move between models with different of clusters. For a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to improve the performance of the MCMC algorithm. We employ proposed algorithms to simulated data as well as a real data example, and the results demonstrate the desired performance of the new sampler.
Luo Y, Stephens DA, 2021, Bayesian inference for continuous-time hidden Markov models with an unknown number of states, Statistics and Computing, Vol: 31, ISSN: 0960-3174
<jats:title>Abstract</jats:title><jats:p>We consider the modeling of data generated by a latent continuous-time Markov jump process with a state space of finite but unknown dimensions. Typically in such models, the number of states has to be pre-specified, and Bayesian inference for a fixed number of states has not been studied until recently. In addition, although approaches to address the problem for discrete-time models have been developed, no method has been successfully implemented for the continuous-time case. We focus on reversible jump Markov chain Monte Carlo which allows the trans-dimensional move among different numbers of states in order to perform Bayesian inference for the unknown number of states. Specifically, we propose an efficient split-combine move which can facilitate the exploration of the parameter space, and demonstrate that it can be implemented effectively at scale. Subsequently, we extend this algorithm to the context of model-based clustering, allowing numbers of states and clusters both determined during the analysis. The model formulation, inference methodology, and associated algorithm are illustrated by simulation studies. Finally, we apply this method to real data from a Canadian healthcare system in Quebec.</jats:p>
Luo Y, Stephens DA, Verma A, et al., 2020, Bayesian latent multi-state modeling for nonequidistant longitudinal electronic health records, BIOMETRICS, Vol: 77, Pages: 78-90, ISSN: 0006-341X
Powell GA, Verma A, Luo Y, et al., 2019, Modeling Chronic Obstructive Pulmonary Disease Progression Using Continuous-Time Hidden Markov Models., Stud Health Technol Inform, Vol: 264, Pages: 920-924, ISSN: 0926-9630
Understanding the progression of chronic diseases, such as chronic obstructive pulmonary disease (COPD), is important to inform early diagnosis, personalized care, and health system management. Data from clinical and administrative systems have the potential to advance this understanding, but traditional methods for modelling disease progression are not well-suited to analyzing data collected at irregular intervals, such as when a patient interacts with a healthcare system. We applied a continuous-time hidden Markov model to irregularly-spaced healthcare utilization events and patient-level characteristics in order to analyze the progression through discrete states of 76,888 patients with COPD. A 4-state model allowed classification of patients into interpretable states of disease progression and generated insights about the role of comorbidities, such as cardiovascular diseases, in accelerating severe trajectories. These results can improve the understanding of the evolution of COPD and point to new hypotheses about chronic disease management and comorbidity.
Yuan M, Boston-Fisher N, Luo Y, et al., 2019, A systematic review of aberration detection algorithms used in public health surveillance, JOURNAL OF BIOMEDICAL INFORMATICS, Vol: 94, ISSN: 1532-0464
Luo Y, Stephens DA, Buckeridge DL, 2018, Estimating prevalence using indirect information and Bayesian evidence synthesis, CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, Vol: 46, Pages: 673-689, ISSN: 0319-5724
Verma A, Powell G, Luo Y, et al., 2018, Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models, NeurIPS 2018
Modeling disease progression in healthcare administrative databases iscomplicated by the fact that patients are observed only at irregular intervalswhen they seek healthcare services. In a longitudinal cohort of 76,888 patientswith chronic obstructive pulmonary disease (COPD), we used a continuous-timehidden Markov model with a generalized linear model to model healthcareutilization events. We found that the fitted model provides interpretableresults suitable for summarization and hypothesis generation.
Powell GA, Luo Y, Verma A, et al., 2017, Multivariate and Longitudinal Health System Indicators, Studies in Health Technology and Informatics, Vol: 235, Pages: 266-270, ISSN: 0926-9630
Powell GA, Luo YT, Verma A, et al., 2017, Multidimensional and longitudinal indicators in population health, Pages: 580-583
Within population health information systems, indicators are commonly presented as independent, cross-sectional measures, neglecting the multivariate, longitudinal nature of disease progression and health care use. We use administrative claims data for patients with a previous diagnosis of chronic obstructive pulmonary disease in Montreal, Canada to explore two approaches to facilitating the discovery and interpretation of patterns across indicators and over time. The first approach identifies regional clusters based on patterns across four health service indicators. Our second approach uses a hidden Markov model to analyze individual- level trajectories based on the same four indicators. Both approaches offer additional insights, such as a dual interpretation of low use of general practitioner services. These approaches to the analysis and visualization of health indicators can provide a foundation for information displays that will help decision makers identify areas of concern, predict future disease burden, and implement appropriate policies.
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