Logistic and neural network models for predicting a hospital admission

J. Brian Adams, Yijin Wert

Research output: Contribution to journalArticle

3 Citations (Scopus)

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

Fingerprint

Neural Network Model
Logistics
Logistic Regression Model
Hypothesis Test
Feedforward Neural Networks
Period of time
Probabilistic Model
Admission
Network model
Neural networks
Output
Term
Model
Estimate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Logistic and neural network models for predicting a hospital admission. / Adams, J. Brian; Wert, Yijin.

In: Journal of Applied Statistics, Vol. 32, No. 8, 01.10.2005, p. 861-869.

Research output: Contribution to journalArticle

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