Logistic and neural network models for predicting a hospital admission

Joseph Brian Adams and Yijin Wert

Journal of Applied Statistics, 2005, vol. 32, issue 8, 861-869

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.

Keywords: Neural networks; logistic regression; prediction; hospital admissions; medical informatics (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (1)

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DOI: 10.1080/02664760500080207

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