C-Lace: Computational Model to Predict 30-Day Post-Hospitalization Mortality

Janusz Wojtusiak, Eman Elashkar, Reyhaneh Mogharab Nia


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 Harvard Style

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 - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 169-177. DOI: 10.5220/0006173901690177

in Bibtex Style

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 - Volume 5: HEALTHINF, (BIOSTEC 2017)},

in EndNote Style

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - C-Lace: Computational Model to Predict 30-Day Post-Hospitalization Mortality
SN - 978-989-758-213-4
AU - Wojtusiak J.
AU - Elashkar E.
AU - Mogharab Nia R.
PY - 2017
SP - 169
EP - 177
DO - 10.5220/0006173901690177