TY - GEN
T1 - Benchmarking Predictive Models in Electronic Health Records
T2 - 34th International Conference on Advanced Information Networking and Applications, AINA 2020
AU - Alsinglawi, Belal
AU - Alnajjar, Fady
AU - Mubin, Omar
AU - Novoa, Mauricio
AU - Karajeh, Ola
AU - Darwish, Omar
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
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.
UR - http://www.scopus.com/inward/record.url?scp=85083693766&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083693766&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-44041-1_24
DO - 10.1007/978-3-030-44041-1_24
M3 - Conference contribution
AN - SCOPUS:85083693766
SN - 9783030440404
T3 - Advances in Intelligent Systems and Computing
SP - 258
EP - 267
BT - Advanced Information Networking and Applications - Proceedings of the 34th International Conference on Advanced Information Networking and Applications, AINA 2020
A2 - Barolli, Leonard
A2 - Amato, Flora
A2 - Moscato, Francesco
A2 - Enokido, Tomoya
A2 - Takizawa, Makoto
PB - Springer
Y2 - 15 April 2020 through 17 April 2020
ER -