Benchmarking predictive models in electronic health records : sepsis length of stay prediction

Belal Alsinglawi; Fady Alnajjar; Omar Mubin; Mauricio Novoa; Ola Karajeh; Omar Darwish

doi:10.1007/978-3-030-44041-1_24

Benchmarking predictive models in electronic health records : sepsis length of stay prediction

Belal Alsinglawi
, Fady Alnajjar
, Omar Mubin
, Mauricio Novoa
, Ola Karajeh
, Omar Darwish

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

7 Citations (Scopus)

Abstract

Forecasting Sepsis length of stay is a challenge for hospitals worldwide. Although there are many attempts to improve sepsis length of stay prediction; however, there is still lack of baselining prediction metrics that can give better results for sepsis length of stay prediction in management hospital systems. This paper introduces a research architecture to predict and benchmark the Length of Stay (LOS) for Sepsis diagnoses from electronic medical records using the machine learning models. The architecture considered the time factor to identify the outperforming algorithms for Sepsis LOS prediction. This work contributes to the field of predictive modelling and information visualization for hospital management systems. Our results showed that the ensemble methods in particular the random forest (RF) outdo other classification models to predict the LOS for Sepsis from electronic medical records for Intensive Care Unit "ICU"-based hospitalizations.

Original language	English
Title of host publication	Advanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020), 15-17 April 2020, Caserta, Italy
Publisher	Springer
Pages	258-267
Number of pages	10
ISBN (Print)	9783030440404
DOIs	https://doi.org/10.1007/978-3-030-44041-1_24
Publication status	Published - 2020
Event	International Conference on Advanced Information Networking and Applications - Duration: 15 Apr 2020 → …

Publication series

Name
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Advanced Information Networking and Applications
Period	15/04/20 → …

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

data processing
forecasting
information visualization
machine learning
medical records
septicemia

Access to Document

10.1007/978-3-030-44041-1_24

Cite this

Alsinglawi, B., Alnajjar, F., Mubin, O., Novoa, M., Karajeh, O., & Darwish, O. (2020). Benchmarking predictive models in electronic health records : sepsis length of stay prediction. In Advanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020), 15-17 April 2020, Caserta, Italy (pp. 258-267). Springer. https://doi.org/10.1007/978-3-030-44041-1_24

Alsinglawi, Belal ; Alnajjar, Fady ; Mubin, Omar et al. / Benchmarking predictive models in electronic health records : sepsis length of stay prediction. Advanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020), 15-17 April 2020, Caserta, Italy. Springer, 2020. pp. 258-267

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title = "Benchmarking predictive models in electronic health records : sepsis length of stay prediction",

abstract = "Forecasting Sepsis length of stay is a challenge for hospitals worldwide. Although there are many attempts to improve sepsis length of stay prediction; however, there is still lack of baselining prediction metrics that can give better results for sepsis length of stay prediction in management hospital systems. This paper introduces a research architecture to predict and benchmark the Length of Stay (LOS) for Sepsis diagnoses from electronic medical records using the machine learning models. The architecture considered the time factor to identify the outperforming algorithms for Sepsis LOS prediction. This work contributes to the field of predictive modelling and information visualization for hospital management systems. Our results showed that the ensemble methods in particular the random forest (RF) outdo other classification models to predict the LOS for Sepsis from electronic medical records for Intensive Care Unit {"}ICU{"}-based hospitalizations.",

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author = "Belal Alsinglawi and Fady Alnajjar and Omar Mubin and Mauricio Novoa and Ola Karajeh and Omar Darwish",

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doi = "10.1007/978-3-030-44041-1\_24",

language = "English",

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Alsinglawi, B, Alnajjar, F, Mubin, O , Novoa, M, Karajeh, O & Darwish, O 2020, Benchmarking predictive models in electronic health records : sepsis length of stay prediction. in Advanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020), 15-17 April 2020, Caserta, Italy. Springer, pp. 258-267, International Conference on Advanced Information Networking and Applications, 15/04/20. https://doi.org/10.1007/978-3-030-44041-1_24

Benchmarking predictive models in electronic health records : sepsis length of stay prediction. / Alsinglawi, Belal; Alnajjar, Fady; Mubin, Omar et al.
Advanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020), 15-17 April 2020, Caserta, Italy. Springer, 2020. p. 258-267.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Benchmarking predictive models in electronic health records : sepsis length of stay prediction

AU - Alsinglawi, Belal

AU - Alnajjar, Fady

AU - Mubin, Omar

AU - Novoa, Mauricio

AU - Karajeh, Ola

AU - Darwish, Omar

PY - 2020

Y1 - 2020

N2 - Forecasting Sepsis length of stay is a challenge for hospitals worldwide. Although there are many attempts to improve sepsis length of stay prediction; however, there is still lack of baselining prediction metrics that can give better results for sepsis length of stay prediction in management hospital systems. This paper introduces a research architecture to predict and benchmark the Length of Stay (LOS) for Sepsis diagnoses from electronic medical records using the machine learning models. The architecture considered the time factor to identify the outperforming algorithms for Sepsis LOS prediction. This work contributes to the field of predictive modelling and information visualization for hospital management systems. Our results showed that the ensemble methods in particular the random forest (RF) outdo other classification models to predict the LOS for Sepsis from electronic medical records for Intensive Care Unit "ICU"-based hospitalizations.

AB - Forecasting Sepsis length of stay is a challenge for hospitals worldwide. Although there are many attempts to improve sepsis length of stay prediction; however, there is still lack of baselining prediction metrics that can give better results for sepsis length of stay prediction in management hospital systems. This paper introduces a research architecture to predict and benchmark the Length of Stay (LOS) for Sepsis diagnoses from electronic medical records using the machine learning models. The architecture considered the time factor to identify the outperforming algorithms for Sepsis LOS prediction. This work contributes to the field of predictive modelling and information visualization for hospital management systems. Our results showed that the ensemble methods in particular the random forest (RF) outdo other classification models to predict the LOS for Sepsis from electronic medical records for Intensive Care Unit "ICU"-based hospitalizations.

KW - data processing

KW - forecasting

KW - information visualization

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KW - medical records

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BT - Advanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020), 15-17 April 2020, Caserta, Italy

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Alsinglawi B, Alnajjar F, Mubin O , Novoa M, Karajeh O, Darwish O. Benchmarking predictive models in electronic health records : sepsis length of stay prediction. In Advanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020), 15-17 April 2020, Caserta, Italy. Springer. 2020. p. 258-267 doi: 10.1007/978-3-030-44041-1_24

Benchmarking predictive models in electronic health records : sepsis length of stay prediction

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