TY - JOUR
T1 - Logistic and neural network models for predicting a hospital admission
AU - Adams, Joseph Brian
AU - Wert, Yijin
N1 - Funding Information:
The analyses upon which this publication is based were performed under Contract Number 500-96-P708, entitled, ‘Utilization and Quality Peer Review Organization for the Commonwealth of Pennsylvania,’ sponsored by the Centers for Medicare and Medicaid Services, Department of Health and Human Services.
PY - 2005/10
Y1 - 2005/10
N2 - 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.
AB - 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.
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U2 - 10.1080/02664760500080207
DO - 10.1080/02664760500080207
M3 - Article
AN - SCOPUS:31144468052
SN - 0266-4763
VL - 32
SP - 861
EP - 869
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 8
ER -