J. Pers. Med. 2012, 2(4), 138-148; doi:10.3390/jpm2040138
Article

A Database-driven Decision Support System: Customized Mortality Prediction

Leo Anthony Celi 1,* email, Sean Galvin 2 email, Guido Davidzon 3 email, Joon Lee 1 email, Daniel Scott 1 email and Roger Mark 1 email
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.
Received: 2 August 2012; in revised form: 9 September 2012 / Accepted: 13 September 2012 / Published: 27 September 2012
(This article belongs to the Special Issue Untold Stories about Personalized Medicine: If You Thought Personalized Medicine Is for Others to Worry about, Think Again)
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Abstract: 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.
Keywords: decision support; intensive care; clinical database; MIMIC; informatics

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Cite This Article

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.

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.

Chicago/Turabian Style

Celi, Leo Anthony; Galvin, Sean; Davidzon, Guido; Lee, Joon; Scott, Daniel; Mark, Roger. 2012. "A Database-driven Decision Support System: Customized Mortality Prediction." J. Pers. Med. 2, no. 4: 138-148.

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