Five-year trajectories of multimorbidity patterns in an elderly Mediterranean population using Hidden Markov Models

Mining Trajectory Patterns Using Hidden Markov Models

Many studies of spatiotemporal pattern discovery partition data space into disjoint cells for effective processing. However, the discovery accuracy of the space-partitioning schemes highly depends on space granularity. Moreover, it cannot describe data statistics well when data spreads over not only one but many cells. In this study, we introduce a novel approach which takes advantages of the e...

AN APPLICATION OF TRAJECTORIES AMBIGUITY IN TWO-STATE MARKOV CHAIN

In this paper, the ambiguity of nite state irreducible Markov chain trajectories is reminded and is obtained for two state Markov chain. I give an applicable example of this concept in President election

Using Hidden Markov Models to Characterize Disease Trajectories

A novel approach is developed for predicting body trajectories for cancer progression, where conditional probabilities of clinical data are modeled using Hidden Markov Model techniques. Basically, each potential body site is encoded by an N-letter code, and a disease trajectory is described in terms of a string of letters. Patient data base records are then represented by such strings with diff...

On Markov and Hidden Markov Models with Applications to Trajectories

Analysis of Temporal Clinical Patterns using Hidden Markov Models

Analysis of complex clinical data in Electronic Health Record (EHR) using machine learning methods may help us to better understand the dynamics of the disease and importance of various patient management interventions. In this work, we study the problem of modeling the dynamics of postsurgical cardiac patient population. Our approach relies on the hidden Markov model to model temporal dynamics...