Length of Hospital Stay Prediction through Unorganised Turing Machines

Luigi Lella, Ignazio Licata

Abstract

Length of hospital stay (LoS) prediction is one of the most important goals in Health Informatics, due to the fact that through this it is possible to optimize the management of health structure resources. In Italian local healthcare systems we are experimenting an health cost containment process and the minimization of care costs is considered an important objective to be achieved. For this reason we have tested several datamining models trained with hospital discharge data, capable to make accurate LoS predictions. In another work we have reached encouraging results by the use of unsupervised models which detect autonomously the subset of non-class attributes to be considered in these classification tasks. Here we are interested in studying also another intelligent data analysis model, the Turing unorganised A-type machine, that is capable to represent the acquired knowledge in a logic formalism. In other terms this solution can explain its predictions by the use of a set of self-acquired knowledge base rules.

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Paper Citation


in Harvard Style

Lella L. and Licata I. (2018). Length of Hospital Stay Prediction through Unorganised Turing Machines.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-281-3, pages 402-407. DOI: 10.5220/0006577804020407


in Bibtex Style

@conference{healthinf18,
author={Luigi Lella and Ignazio Licata},
title={Length of Hospital Stay Prediction through Unorganised Turing Machines},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2018},
pages={402-407},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006577804020407},
isbn={978-989-758-281-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Length of Hospital Stay Prediction through Unorganised Turing Machines
SN - 978-989-758-281-3
AU - Lella L.
AU - Licata I.
PY - 2018
SP - 402
EP - 407
DO - 10.5220/0006577804020407