This document summarizes a research paper that proposed using an ensemble learning approach to predict early hospital mortality for intensive care unit patients. It discusses previous work on mortality prediction that had limitations in handling missing data, imbalanced data, and lack of early prediction. The proposed method uses data mining techniques like random forest, decision trees, naive Bayes and PART on clinical variables from the first 6 hours of ICU patients. It conducted experiments with 10-fold cross validation to evaluate and compare the performance of these techniques as well as existing scoring models. The results showed the ensemble approach achieved higher accuracy than previous methods in early hospital mortality prediction.

1. A review on
Early Hospital Mortality Prediction of Intensive Care Unit
Patients Using an Ensemble Learning Approach
Authors: Awad, A., Bader-El-Den, M., McNicholas, J., & Briggs, J.
By
Reza Sadeghi
Sadeghi.2@ wright.edu
Instructor
Dr. Romine
Nov 2017

2. Outline
• The importance of early mortality prediction
• Previous work pros and cons
• The proposed methods
• Experiments
• Conclusion

3. Early mortality prediction usage
• A decision support systems for intensivists
• The more accurate and quick decision provides the more benefit for
patients and health care resources
• The quality and cost of treatment
• The ICU patient is highly monitored using electronic equipment

4. Previous work pros and cons
Customized models
Score based models
Data mining models
- perform better than traditional scores [1]
- patients may suffering from heterogeneous diseases then selecting the
proper model is hard job
- rely on panels of experts or statistical models
- refined for use within specified geographical areas
- extracting features from Electronic Medical Records
- only few models are designed for early mortality
- low discrimination power
- many of required attributes are not always available at ICU admission
- higher performance rather than the previous methods
- descriptive modelling as it explains hidden clinical implications
- the results of different algorithm on different data set are not constant.
- it depends on the population of interest, the variables measured and the
outcome being tested

5. Missing Data Type:
1. missing completely at random (MCAR)
2. missing at random (MAR)
3. Missing not at random (MNAR) [2]
Missing Data Handling:
1. interpretation
2. ignoring those records from the dataset that
are not complete
3. substitute the missing value by the mean or
mode value of each attribute.
("ReplaceMissing-Values“ filter in weka)
4. predicted by using a learning algorithm, such
as Multiple Imputation or EMImputation [3]
Imbalance data Handling:
1. re-sampling [4,5]: under sampling and
oversampling.
2. making the classier 'cost sensitive' [46]
3. hybrid method
Data mining models issues

6. The structure of experiments
Data set:
MIMIC II
Age of patients:
patients at the age of 16 or above with a single ICU stay
Place of patients:
in either the Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery
Recovery Unit (CSRU)
Early Hospital Mortality Prediction :
define as predicting death inside the hospital via both the chart and lab-test
variables collected in the first 6 hours
Handling missing data :
ignored attributes with coverage below 10%

7. Selected Attributes
Random Forest, Decision
Trees, Naive Bayes and PART
ANN
Selected DM techniques
10 times 10-fold cross-validated
Experiment strategy

8. Experiments A and B

9. Experiments C, D and E

10. The performance of the methods via AUROC

11. The proposed method in contrast to the
scoring models via AUROC

12. Thank you for your attention!