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E-mail: victor. Luis Calvo Mackenna. Antonio Varas In this paper we present a probabilistic model that contributes to the study of dynamics in the behavior and permanence of patients in a cardiovascular intensive care unit. The model corresponds to a discrete Markov Chain, that allows to predict the time that a patient remains in the system through the time, by means of certain severity of illness states and the corresponding transition probabilities between those states.

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E-mail: victor. Luis Calvo Mackenna. Antonio Varas In this paper we present a probabilistic model that contributes to the study of dynamics in the behavior and permanence of patients in a cardiovascular intensive care unit. The model corresponds to a discrete Markov Chain, that allows to predict the time that a patient remains in the system through the time, by means of certain severity of illness states and the corresponding transition probabilities between those states.

The different states are based on the construction of a new score created for this study. We summarize the details of the adopted methodology and the main results reached in the application of the model.

Keywords: Markov chains, probabilistic model, intensive care unit, length of hospital stay, score. CRC Press. Interfaces, Vol. Sonnenberg, J. Medical Decision Making, Vol. Aikawa, S. Fujushima, M. Kobayashi, S.

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Kroenke, C. Callahan, C. Journal of Clinical Epidemiology. Graham, D. Lee, G. Martich, A. Boujoukos, R. Keenan, and B. Tu, S. Jaglal, C. Clermont, D. Angus, S. DiRusso, M. Griffin, W. Critical Care Medicine. Rocco, E. Morgado, E. Scorpio, S. Kapadia, S. Vineberg, C. Kapadia, W. Chan, R. Sachdeva et al. European Journal of Operational Research Vol. Bertsekas, J. Athena Scientific. Academic Press. Recibido el 29 de junio de , aceptado el 6 de junio de Servicios Personalizados Revista.

ABSTRACT In this paper we present a probabilistic model that contributes to the study of dynamics in the behavior and permanence of patients in a cardiovascular intensive care unit.

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Calculating the variance in Markov-processes with random reward

We'd like to understand how you use our websites in order to improve them. Register your interest. In this article we present a generalization of Markov Decision Processes with discreet time where the immediate rewards in every period are not deterministic but random, with the two first moments of the distribution given. Formulas are developed to calculate the expected value and the variance of the reward of the process, which formulas generalize and partially correct other results. We make some observations about the distribution of rewards for processes with limited or unlimited horizon and with or without discounting. Applications with risk sensitive policies are possible; this is illustrated in a numerical example where the results are revalidated by simulation.

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