Logistic and neural network models for predicting a hospital admission

Joseph Brian Adams, Yijin Wert

Research output: Contribution to journalArticle

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)861-869
Number of pages9
JournalJournal of Applied Statistics
Volume32
Issue number8
DOIs
StatePublished - Oct 1 2005

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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