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Authors: Janusz Wojtusiak ; Eman Elashkar and Reyhaneh Mogharab Nia

Affiliation: George Mason University, United States

Keyword(s): Mortality Prediction, Machine Learning, Online Calculator.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Health Information Systems ; Pattern Recognition and Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: This paper describes a machine learning approach to creation of computational model for predicting 30-day post hospital discharge mortality. The Computational Length of stay, Acuity, Comorbidities and Emergency visits (C-LACE) is an attempt to improve accuracy of popular LACE model frequently used in hospital setting. The model has been constructed and tested using MIMIC III data. The model accuracy (AUC) on testing data is 0.74. A simplified, user-oriented version of the model (Minimum C-LACE) based on 20-most important mortality indicators achieves practically identical accuracy to full C-LACE based on 308 variables. The focus of this paper is on detailed analysis of the models and their performance. The model is also available in the form of online calculator.

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Paper citation in several formats:
Wojtusiak, J.; Elashkar, E. and Mogharab Nia, R. (2017). C-Lace: Computational Model to Predict 30-Day Post-Hospitalization Mortality. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF; ISBN 978-989-758-213-4; ISSN 2184-4305, SciTePress, pages 169-177. DOI: 10.5220/0006173901690177

@conference{healthinf17,
author={Janusz Wojtusiak. and Eman Elashkar. and Reyhaneh {Mogharab Nia}.},
title={C-Lace: Computational Model to Predict 30-Day Post-Hospitalization Mortality},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF},
year={2017},
pages={169-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006173901690177},
isbn={978-989-758-213-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - HEALTHINF
TI - C-Lace: Computational Model to Predict 30-Day Post-Hospitalization Mortality
SN - 978-989-758-213-4
IS - 2184-4305
AU - Wojtusiak, J.
AU - Elashkar, E.
AU - Mogharab Nia, R.
PY - 2017
SP - 169
EP - 177
DO - 10.5220/0006173901690177
PB - SciTePress

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