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Logistic and neural network models for predicting a hospital admission

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  • Joseph Brian Adams
  • Yijin Wert

Abstract

Feedforward neural networks are often used in a similar manner as logistic regression models; that is, to estimate the probability of the occurrence of an event. In this paper, a probabilistic model is developed for the purpose of estimating the probability that a patient who has been admitted to the hospital with a medical back diagnosis will be released after only a short stay or will remain hospitalized for a longer period of time. As the purpose of the analysis is to determine if hospital characteristics influence the decision to retain a patient, the inputs to this model are a set of demographic variables that describe the various hospitals. The output is the probability of either a short or long term hospital stay. In order to compare the ability of each method to model the data, a hypothesis test is performed to test for an improvement resulting from the use of the neural network model.

Suggested Citation

  • Joseph Brian Adams & Yijin Wert, 2005. "Logistic and neural network models for predicting a hospital admission," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(8), pages 861-869.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:8:p:861-869
    DOI: 10.1080/02664760500080207
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    References listed on IDEAS

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    1. Joseph Brian Adams, 1999. "Predicting pickle harvests using a parametric feedforward neural network," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(2), pages 165-176.
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    Cited by:

    1. Nader Fallah & Arnold Mitnitski & Kenneth Rockwood, 2011. "Applying neural network Poisson regression to predict cognitive score changes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 2051-2062, November.

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