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. 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.
Another study by Celi et al. REF employed LR, the Bayesian Network (BN) and the Artificial Neural Network (ANN) using MIMIC dataset for predicting the mortality of acute kidney injury (1,400 cases) and Subarachnoid Hemorrhage (223 cases) patients.
A Database-driven Decision Support System: Customized Mortality Prediction
Journal of Personalized Medicine
Journal of Personalized Medicine
Medicine Computer Science