Neural network modeling of the level of observation decision in an acute psychiatric ward

Penny, W D and Frost, D P (1997) Neural network modeling of the level of observation decision in an acute psychiatric ward. Computers and Biomedical Research, 30 (1). pp. 1-17. ISSN 0010-4809

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Abstract

Patients in an acute psychiatric ward need to be observed with varying levels of closeness. We report a series of experiments in which neural networks were trained to model this "level of observation" decision. One hundred eighty-seven such clinical decisions were used to train and test the networks which were evaluated by a multitrial v-fold cross-validation procedure. One neural network modeling approach was to break down the decision process into four subproblems, each of which was solved by a perceptron unit. This resulted in a hierarchical perceptron network having a structure that was equivalent to a sparsely connected two-layer perceptron. Neural network approaches were compared with nearest neighbor, linear regression, and naive Bayes classifiers. The hierarchical and sparse neural networks were the most accurate classifiers. This shows that the decision process is nonlinear, that neural nets can be more accurate than other statistical approaches, and that hierarchical decomposition is a useful methodology for neural network design.

Item Type: Article
Uncontrolled Keywords: acute disease,algorithms,bayes theorem,decision support techniques,hospitalization,humans,linear models,mental disorders,neural networks (computer),psychiatry,comparative study
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 23 Aug 2017 05:04
Last Modified: 25 Jul 2019 00:09
URI: https://ueaeprints.uea.ac.uk/id/eprint/64645
DOI: 10.1006/cbmr.1997.1432

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