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A Database-driven Decision Support System: Customized Mortality Prediction

1
Laboratory of Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Avenue, E25-505, Cambridge, MA 02139, USA
2
Department of Cardiac Surgery, Dunedin Hospital, 201 Great King Street, Dunedin 9054, New Zealand
3
Department of Radiology, Stanford Hospital, 300 Pasteur Drive, Stanford, CA 94305, USA
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2012, 2(4), 138-148; https://doi.org/10.3390/jpm2040138
Received: 2 August 2012 / Revised: 9 September 2012 / Accepted: 13 September 2012 / Published: 27 September 2012
We hypothesize that local customized modeling will provide more accurate mortality prediction than the current standard approach using existing scoring systems. Mortality prediction models were developed for two subsets of patients in Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC), a public de-identified ICU database, and for the subset of patients >80 years old in a cardiac surgical patient registry. Logistic regression (LR), Bayesian network (BN) and artificial neural network (ANN) were employed. The best-fitted models were tested on the remaining unseen data and compared to either the Simplified Acute Physiology Score (SAPS) for the ICU patients, or the EuroSCORE for the cardiac surgery patients. Local customized mortality prediction models performed better as compared to the corresponding current standard severity scoring system for all three subsets of patients: patients with acute kidney injury (AUC = 0.875 for ANN, vs. SAPS, AUC = 0.642), patients with subarachnoid hemorrhage (AUC = 0.958 for BN, vs. SAPS, AUC = 0.84), and elderly patients undergoing open heart surgery (AUC = 0.94 for ANN, vs. EuroSCORE, AUC = 0.648). Rather than developing models with good external validity by including a heterogeneous patient population, an alternative approach would be to build models for specific patient subsets using one’s local database. View Full-Text
Keywords: decision support; intensive care; clinical database; MIMIC; informatics decision support; intensive care; clinical database; MIMIC; informatics
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MDPI and ACS Style

Celi, L.A.; Galvin, S.; Davidzon, G.; Lee, J.; Scott, D.; Mark, R. A Database-driven Decision Support System: Customized Mortality Prediction. J. Pers. Med. 2012, 2, 138-148. https://doi.org/10.3390/jpm2040138

AMA Style

Celi LA, Galvin S, Davidzon G, Lee J, Scott D, Mark R. A Database-driven Decision Support System: Customized Mortality Prediction. Journal of Personalized Medicine. 2012; 2(4):138-148. https://doi.org/10.3390/jpm2040138

Chicago/Turabian Style

Celi, Leo Anthony, Sean Galvin, Guido Davidzon, Joon Lee, Daniel Scott, and Roger Mark. 2012. "A Database-driven Decision Support System: Customized Mortality Prediction" Journal of Personalized Medicine 2, no. 4: 138-148. https://doi.org/10.3390/jpm2040138

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