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Open AccessArticle

Decision Support Software for Forecasting Patient’s Length of Stay

1
Department of Computer & Informatics Engineering, Technological Educational Institute of Western Greece, 263-34 GR Antirion, Greece
2
Department of Business Administration (LAIQDA Lab), Technological Educational Institute of Peloponnese, GR 241-00 Kalamata, Greece
3
Department of Mathematics, University of Patras, 265-00 GR Patras, Greece
*
Author to whom correspondence should be addressed.
Algorithms 2018, 11(12), 199; https://doi.org/10.3390/a11120199
Received: 11 October 2018 / Revised: 4 December 2018 / Accepted: 4 December 2018 / Published: 6 December 2018
(This article belongs to the Special Issue Humanistic Data Mining: Tools and Applications)
Length of stay of hospitalized patients is generally considered to be a significant and critical factor for healthcare policy planning which consequently affects the hospital management plan and resources. Its reliable prediction in the preadmission stage could further assist in identifying abnormality or potential medical risks to trigger additional attention for individual cases. Recently, data mining and machine learning constitute significant tools in the healthcare domain. In this work, we introduce a new decision support software for the accurate prediction of hospitalized patients’ length of stay which incorporates a novel two-level classification algorithm. Our numerical experiments indicate that the proposed algorithm exhibits better classification performance than any examined single learning algorithm. The proposed software was developed to provide assistance to the hospital management and strengthen the service system by offering customized assistance according to patients’ predicted hospitalization time. View Full-Text
Keywords: Length of stay; data mining; two-level classifier; healthcare decision support; healthcare management; classification Length of stay; data mining; two-level classifier; healthcare decision support; healthcare management; classification
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Livieris, I.E.; Kotsilieris, T.; Dimopoulos, I.; Pintelas, P. Decision Support Software for Forecasting Patient’s Length of Stay. Algorithms 2018, 11, 199.

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