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

Janusz Wojtusiak, Eman Elashkar, Reyhaneh Mogharab Nia

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.

References

  1. AHRQ (2016) https://www.hcup-us.ahrq.gov/tools software/ccs/ccs.jsp
  2. Au, AG, McAlister, FA, Bakal, JA, et al. 2012. Predicting the Risk of Unplanned Readmission or Death within 30 Days of Discharge After a Heart Failure Hospitalization. American Heart Journal. 164(3):365- 372.
  3. Breiman, L., 2001. Random forests. Machine learning, 45(1), 5-32.
  4. Cooper, Gregory F., et al., 1997. An evaluation of machinelearning methods for predicting pneumonia mortality. Artificial intelligence in medicine 9.2. 107-138.
  5. Davis, RB, Iezzoni LI, Phillips RS, Reiley P, et al. 1995. Predicting in-hospital mortality: the importance of functional status information. Med Care.33:906-921.
  6. Goldberger AL, et al., 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215. full
  7. Gu, W., Vieira, A. R., Hoekstra, R. M., Griffin, P. M., & Cole, D. , 2015. Use of random forest to estimate population attributable fractions from a case-control study of Salmonella enterica serotype Enteritidis infections. Epidemiology and Infection, 1-9.
  8. Ho KM, Dobb GJ, Knuiman M, et al. 2006. A comparison of admission and worst 24-hour Acute Physiology and Chronic Health Evaluation II scores in predicting hospital mortality: a retrospective cohort study. Crit Care; 10:R4.
  9. Iezzoni LI. Risk Adjustment for Measuring Health Outcomes Ann Arbor. 1994. Mich: Health Administration Press.
  10. Inouye, S.K., Peduzzi, P., Robison, J., Hughes, J., Horwitz, R., Concato, J, MPH, 1998. Importance of Functional Measures in Predicting Mortality Among Older Hospitalized Patients. JAMA. 1998;279(15):1187- 1193. doi:10.1001/jama.279.15.1187.
  11. Johnson AEW, Pollard TJ, Shen L, Lehman L, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, and Mark RG. 2016. MIMIC-III, a freely accessible critical care database. Scientific Data. 10.1038/sdata.2016.35.
  12. Kuzniewicz MW1, Vasilevskis EE, Lane R, Dean ML, Trivedi NG, Rennie DJ, Clay T, Kotler PL, Dudley RA. 2008. Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders. Chest. 2008;133(6):1319-27. doi: 10.1378/chest.07-3061.
  13. Levy, C., Kheirbek, R,, Alemi, F., Wojtusiak, J., Sutton, B,, Williams, A.R. and Williams, A., 2015. Predictors of six-month mortality among nursing home residents: diagnoses may be more predictive than functional disability. Journal of Palliative Medicine, 18(2), 100-6.
  14. Moreno, RP, Metnitz, P.G., Almeida, E, et al., 2005. SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med; 31:1345.
  15. Moskovitch, R. and Shahar, Y., 2015. Classification-driven temporal discretization of multivariate time series. Data Mining and Knowledge Discovery, 29(4), pp.871-913.
  16. Ngufor, C., Wojtusiak, J., Hooker, A., Oz, T. and Hadley, J., 2014. Extreme Logistic Regression: A Large Scale Learning Algorithm with Application to Prostate Cancer Mortality Prediction. Proceedings of the 27th International Florida Artificial Intelligence Research Society Conference.
  17. Rocker, G., Cook, D., Sjokvist, V., Weaver, B., Finfer, S., McDonald, E., Marshall, J., Kirby, A., Levy, M., at al., 2004. Clinician Predictions of Intensive Care Unit Mortality, Crit Care Med. 2004;32 (5)1
  18. Rose, Sherri, 2013. "Mortality risk score prediction in an elderly population using machine learning." American journal of epidemiology 177.5. 443-452.
  19. Taylor, R. Andrew, et al. 2016. Prediction of In- hospital Mortality in Emergency Department Patients with Sepsis: A Local Big Data-Driven, Machine Learning Approach. Academic Emergency Medicine.
  20. Van Walraven, C., Dhalla, IA, Bell, C., et al., 2010. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ 2010 Apr 6; 182(6): 551-557. doi: 10.1503/cmaj.091117. PMCID: PMC2845681
  21. Van Walraven C, Wong J, Forster AJ. 2012. LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med. 2012 Jul 19;6(3):e80-90. PMCID: PMC3659212
  22. Vasilevskis EE, Kuzniewicz MW, Cason BA, Lane RK, Dean ML, Clay T, Rennie DJ, Vittinghoff E, Dudley RA. 2009. Mortality probability model III and simplified acute physiology score II: assessing their value in predicting length of stay and comparison to APACHE IV. Chest. 136(1):89-101. doi: 10.1378/chest.08-2591.
  23. Verduijn, M., Sacchi, L., Peek, N., Bellazzi, R., de Jonge, E. and de Mol, B.A., 2007. Temporal abstraction for feature extraction: A comparative case study in prediction from intensive care monitoring data. Artificial intelligence in medicine, 41(1), pp.1-12.
  24. Wojtusiak, J., Elashkar, E., Mogharab, R., 2016. Integrating Complex Health Data for Analytics. The Machine Learning and Inference Laboratory, electronic circulation. MLI 16-1.
Download


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

@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 - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={169-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006173901690177},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
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