research-article

Predicting length of stay in hospitalized patients using SSL algorithms

Authors:

Ioannis E. Livieris,

Ioannis F. Dimopoulos,

Theodore Kotsilieris,

Panagiotis PintelasAuthors Info & Claims

DSAI '18: Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion

Pages 16 - 22

https://doi.org/10.1145/3218585.3218588

Published: 20 June 2018 Publication History

Abstract

Length of stay in hospitalized patients is acknowledged as a critical factor for healthcare policy planning that consequently affects the available human, technical and financial resources as well as facilities occupation. Over recent years, data mining and machine learning led to the development of several efficient and accurate models for predicting of how long a patient will stay in the hospital and support healthcare policy planning. As an alternative to traditional classification methods, semi-supervised learning algorithms have become a hot topic of significant research which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we evaluate the performance of semi-supervised methods in predicting the length of stay of hospitalized patients. Our reported experimental results illustrate that a good predictive accuracy can be achieved using few labeled data in comparison to well known supervised learning algorithms.

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Cited By

Agarwal ABanerjee TRomine WThirunarayan KChen LCajita M(2023)Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure2023 IEEE International Conference on Digital Health (ICDH)10.1109/ICDH60066.2023.00038(208-216)Online publication date: Jul-2023
https://doi.org/10.1109/ICDH60066.2023.00038
Kasten J(2022)Big Data Applications in Healthcare AdministrationResearch Anthology on Big Data Analytics, Architectures, and Applications10.4018/978-1-6684-3662-2.ch048(1003-1034)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3662-2.ch048
Gurazada SGao SBurstein FBuntine P(2022)Predicting Patient Length of Stay in Australian Emergency Departments Using Data MiningSensors10.3390/s2213496822:13(4968)Online publication date: 30-Jun-2022
https://doi.org/10.3390/s22134968
Show More Cited By

Index Terms

Predicting length of stay in hospitalized patients using SSL algorithms
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Semi-supervised learning

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cover image ACM Other conferences

DSAI '18: Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion

June 2018

365 pages

ISBN:9781450364676

DOI:10.1145/3218585

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 20 June 2018

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DSAI 2018

DSAI 2018: 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion

June 20 - 22, 2018

Thessaloniki, Greece

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DSAI '18 Paper Acceptance Rate 17 of 23 submissions, 74%;

Overall Acceptance Rate 17 of 23 submissions, 74%

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Cited By

Agarwal ABanerjee TRomine WThirunarayan KChen LCajita M(2023)Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure2023 IEEE International Conference on Digital Health (ICDH)10.1109/ICDH60066.2023.00038(208-216)Online publication date: Jul-2023
https://doi.org/10.1109/ICDH60066.2023.00038
Kasten J(2022)Big Data Applications in Healthcare AdministrationResearch Anthology on Big Data Analytics, Architectures, and Applications10.4018/978-1-6684-3662-2.ch048(1003-1034)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3662-2.ch048
Gurazada SGao SBurstein FBuntine P(2022)Predicting Patient Length of Stay in Australian Emergency Departments Using Data MiningSensors10.3390/s2213496822:13(4968)Online publication date: 30-Jun-2022
https://doi.org/10.3390/s22134968
Stone KZwiggelaar RJones PMac Parthaláin N(2022)A systematic review of the prediction of hospital length of stay: Towards a unified frameworkPLOS Digital Health10.1371/journal.pdig.00000171:4(e0000017)Online publication date: 14-Apr-2022
https://doi.org/10.1371/journal.pdig.0000017
Bacchi STan YOakden‐Rayner LJannes JKleinig TKoblar S(2021)Machine learning in the prediction of medical inpatient length of stayInternal Medicine Journal10.1111/imj.1496252:2(176-185)Online publication date: 27-Oct-2021
https://doi.org/10.1111/imj.14962
Grampurohit SSunkad S(2020)Hospital Length of Stay Prediction using Regression Models2020 IEEE International Conference for Innovation in Technology (INOCON)10.1109/INOCON50539.2020.9298294(1-5)Online publication date: 6-Nov-2020
https://doi.org/10.1109/INOCON50539.2020.9298294
Livieris IKotsilieris TDimopoulos IPintelas P(2018)Decision Support Software for Forecasting Patient’s Length of StayAlgorithms10.3390/a1112019911:12(199)Online publication date: 6-Dec-2018
https://doi.org/10.3390/a11120199

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