Next Article in Journal
Special Issue on Algorithms for the Resource Management of Large Scale Infrastructures
Previous Article in Journal
Damage Identification Algorithm of Hinged Joints for Simply Supported Slab Bridges Based on Modified Hinge Plate Method and Artificial Bee Colony Algorithms
Previous Article in Special Issue
Solon: A Holistic Approach for Modelling, Managing and Mining Legal Sources
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(12), 199; https://doi.org/10.3390/a11120199

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.
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)
Full-Text   |   PDF [1046 KB, uploaded 6 December 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Livieris, I.E.; Kotsilieris, T.; Dimopoulos, I.; Pintelas, P. Decision Support Software for Forecasting Patient’s Length of Stay. Algorithms 2018, 11, 199.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top