Fig 1.
Pre-incision variables chosen for predictive analytical modeling from a data base of 185 cardiac surgical patients.
Table 1.
ALM Fractional Contributions to the Final Correlation with ICU LOS.
Fig 2.
The correlation between the t statistic and the fractional importance of the 8 pre-incision factors that had the strongest associations with ICU length of stay.
Fig 3.
A. ALM Training Model for Prediction of ICU LOS. The regression relationship between the predicted ICU length of stay from 8 pre-incision factors and the observed ICU length of stay from the training model generated by the SPSS Automatic Linear Modeling module. B. ALM Cross-Validated Predictions for ICU LOS.
The cross-validated result of the training model when applied to new ("untrained") patient data.
Fig 4.
The architecture of the typical neural network utilized in the SPSS Artificial Neural Network module for the 8 pre-incision factors.
Fig 5.
A. ANN Training Model for Prediction of ICU LOS. The regression relationship between the predicted ICU length of stay from 8 pre-incision factors and the observed ICU length of stay from the training model generated by the SPSS Artificial Neural Network module. B. ANN Prediction Results for ICU LOS for Untrained Data.
The cross-validated result of the training model when applied to new ("untrained") patient data.
Fig 6.
The prediction weights generated by the neural network for each interaction among the 8 pre-incision factors ("input layer") and the 4 nodes ("hidden layer"), and the output weights of each node to the prediction of ICU length of stay; bias weights are also contributed from the input layer and the hidden layer.
Fig 7.
The relative importance of the 8 pre-incision factors to the anticipation of ICU length of stay (generated from the Artificial Neural Network algorithm).
Table 2.
Odds Ratios for Prediction that a Patient would have ICU LOS in the Upper Quartile of Observed Data from both the Trained and Cross-Validated ANN Analyses.
Fig 8.
The Final Decision Tree Model that was Assessed for Prediction of ICU LOS.
Fig 9.
A. Decision Tree Training Model for Prediction of ICU LOS. The regression relationship between the predicted ICU length of stay from 7 pre-incision factors and the observed ICU length of stay from the training model generated by the SPSS Decision Tree module. B. Cross-Validated Predictions for ICU LOS.
The cross-validated result of the training model when applied to new ("untrained") patient data.
Fig 10.
A. Random Forest Training Model for Prediction of ICU LOS. The regression relationship between the predicted ICU length of stay from 8 pre-incision factors and the observed ICU length of stay from the training model. B. Cross-Validated Predictions for ICU LOS.
The cross-validated result of the training model when applied to new ("untrained") patient data.
Table 3.
Comparison of the Accuracies for the Four Models used to Predict ICU LOS.
Table 4.
Fractional Importance differences between ALM and ANN.