๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Continuous-time Markov models for geriatric patient behaviour

โœ Scribed by Taylor, Gordon ;McClean, Sally ;Millard, Peter


Publisher
John Wiley and Sons
Year
1997
Tongue
English
Weight
101 KB
Volume
13
Category
Article
ISSN
8755-0024

No coin nor oath required. For personal study only.

โœฆ Synopsis


Previous research has shown that the flow of patients around departments of geriatric medicine and ex-patients in the community may be modelled by the application of a mixed exponential distribution where the number of terms in the mixture corresponds to the number of stages of patient care. A common scenario is that there are two stages for in-patient care (acute and long stay) and one for ex-patients in the community. However, current hospital planning models assume that patients all move through the system at the same rate, i.e. a one-compartment approach, thereby ignoring the effects of inherent heterogeneity for individual patients in the system -much of which may be explained by considering patient care as comprising a number of states. This paper uses a continuous-time Markov model to describe the movement of a cohort of patients entering the system at time t"0. The modelling of in-patient geriatric care has already been considered. Our present approach enables us to study the whole system of geriatric care and therefore not only to look at the time patients spend in hospital but also the subsequent time patients spend in the community. The model is fitted to data from St George's Hospital, London, consisting of data from 6994 geriatric patients admitted between 1969 and 1984. The model is fitted using the method of maximum likelihood, unlike previous work that has applied the method of least squares.


๐Ÿ“œ SIMILAR VOLUMES


Using a continuous-time Markov model wit
โœ Taylor, G. J. ;McClean, S. I. ;Millard, P. H. ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 96 KB ๐Ÿ‘ 1 views

The population of geriatrics in a given hospital district is relatively stable and therefore we may model the movement of geriatric patients by considering both their stays in hospital and subsequent releases back into the community. The care of the elderly in departments of geriatric medicine may b

Non-homogeneous continuous-time Markov a
โœ McClean, Sally ;Montgomery, Erin ;Ugwuowo, Fidelis ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 94 KB ๐Ÿ‘ 1 views

We develop estimation methods for continuous-time Markov and semi-Markov non-homogeneous manpower systems using the notion of calendar time divided into 'time windows' by change points. The model parameters may only change at these change points but remain constant between them. Our estimation metho

Markov models for time series with mixed
โœ Gary K. Grunwald; Richard H. Jones ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 161 KB

We consider modelling time series of amounts which may be zero using a stochastic ยฎrst-order Markov model with mixed transition density having a discrete component at 0 and a continuous component describing non-zero amounts. The models extend chain-dependent stochastic models in the literature on mo

A compound measure of dependability for
โœ Attila Csenki ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 412 KB ๐Ÿ‘ 1 views

The Markov analysis of reliability models frequently involves a partitioning of the state space into two or more subsets, each corresponding to a given degree of functionality of the system. A common partitioning is G U B U { W ) , where G (good) and B (bad) stand, respectively, for fully and partia

A Multiple Binary Markov Chain Model for
โœ D.K. Morris; G.R. Wood; W.P. Baritompa; A.G. Keen ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 260 KB

The behaviour of many biological systems can be attributed to that of a large number of units, with each unit swinging between two competing states. During the past few years efforts have been made (e.g., Chung and Kennedy, 1996) to describe such discrete systems using a multiple binary Markov chain