This document outlines a proposed method for early mortality prediction in ICU patients using only the initial hour of heart rate signal data. It discusses challenges like missing data, the importance of early prediction for patient care and costs, and limitations of previous approaches. The method uses signal processing and feature extraction from heart rate data, followed by classification models to predict mortality risk. Evaluation on MIMIC-III data showed interpretable decision tree models achieved good performance for early ICU mortality prediction.

1. Early Hospital Mortality Prediction using
Vital Signals
By
Reza Sadeghi
http://knoesis.org/Reza

2. 2
Outline
• The importance of early hospital mortality prediction
• Challenges in early mortality prediction in ICU patients
• Previous work pros and cons
• Our proposed methods
• Experiments
• Conclusion

3. • More accurate and quick decision provides more benefit for patients and
health care resources
1. ICU departments consume 22% of total hospital costs in the United States [1]
2. Increasing ICU length of stay is associated with higher 1-year mortality [2]
[1] Tan, S. S., Bakker, J., Hoogendoorn, M. E., Kapila, A., Martin, J., Pezzi, A., ... & Hakkaart-van Roijen, L. (2012). Direct cost analysis of intensive care
unit stay in four European countries: applying a standardized costing methodology. Value in health, 15(1), 81-86.
[2] Moitra, V. K., Guerra, C., Linde-Zwirble, W. T., & Wunsch, H. (2016). Relationship between ICU length of stay and long-term mortality for elderly ICU
survivors. Critical care medicine, 44(4), 655.
3
Why early mortality prediction is important?

4. • The highly monitoring ICU patient’s provides huge amount of data
4
How can we contribute to this field?
• Increasing the intensivists staffs is not associated with improved mortality
• The higher quality and lower cost of treatment

5. Challenges in early mortality prediction
in ICU patients
• Big data
2.4 TB The MIMIC-III Waveform Database Matched Subset
• Real time application
• Costly fault
• Missing data
• Comorbidity
5

6. Hot topic
6

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

8. Our main contributions
• No dependency on many clinical records which contain missing
values
• A signal-based model for early mortality prediction
• Faster feedback to healthcare professionals
• A clinical decision support system which focuses on using only the
initial one hour of heart rate signal
8

9. MIMIC-III
9
The age distribution over the Whole MIMIC-III and the
Matched Subset
• MIMIC-III database comprising the
records of 46520 patients (Clinical
Database records)
• The Matched Subset contains records of
10282 patients (Waveform Database
records)
• 2.4 TB The MIMIC-III Waveform
Database Matched Subset

10. Causality
10
diseases of the
circulatory system
• CCU: Coronary (or cardiac) Intensive Care Unit that takes patients who have
cardiac-related problems
• High heart rate has been shown to be an independent risk factor for all-cause
and cardiovascular death in general population studies [1]
• Epidemiological studies have reported increased risk of cardiovascular disease,
cancer and all-cause mortality with greater resting heart rate [2]
[1]https://www.medicographia.com/2010/07/recommendations-on-how-to-measure-resting-heart-rate/
[2] Aune, D., Sen, A., ó'Hartaigh, B., Janszky, I., Romundstad, P. R., Tonstad, S., & Vatten, L. J. (2017). Resting heart rate and
the risk of cardiovascular disease, total cancer, and all-cause mortality–A systematic review and dose–response meta-analysis of
prospective studies. Nutrition, Metabolism and Cardiovascular Diseases, 27(6), 504-517

11. Signal processing
11
• Handling noise due to different recording systems
Moving average filtering
• Fair comparison signals with different sampling rates and lengths
Resampling using the anti-aliasing finite impulse response low-pass filter
Considering only the initial one hour

12. Feature extraction
Each signal is described in terms of 12 statistical
and signal-based features
12
Column Feature
Passed away
patients
Alive
patients
1 Maximum 97.82 90.92
2 Minimum 80.69 76.24
3 Mean 88.46 81.92
4 Median 88.45 81.81
5 Mode 85.25 79.98
6 Standard deviation 2.63 2.25
7 Variance 15.84 11.56
8 Range 17.13 14.68
9 Kurtosis 17.48 17.85
10 Skewness 0.83 1.02
11 Averaged power 8186.02 7045.04
12 Energy spectral density 5114.78 4420.38

13. Unbiased and efficient modeling
• Avoid biasing toward majority class (Imbalance data challenging)
Using adaptive semi-unsupervised weighted oversampling (A-SUWO)
• Robust against time dependency of samples
Design experiments based on 10-fold cross-validation strategy
• Handling high computational complexity of big data
Leveraging parallel programming
• Efficiency vs. Transparency
Comparing both black-box and interpretable classifiers
13

14. Classification
14
Classifier Precision Recall F1-score Interpretability
Random forest 0.97 0.97 0.97 Hard
Gaussian SVM 0.95 0.96 0.96 Hard
Decision tree 0.90 0.92 0.91 Easy
Boosted trees 0.91 0.83 0.87 Hard
K-NN 0.80 0.85 0.82 Hard
Logistic regression 0.77 0.67 0.72 Easy
Linear
Discriminant
0.78 0.66 0.71 Easy
Linear SVM 0.80 0.63 0.70 Easy

15. Transparency
15
Gini index
𝐺𝐷𝐼 = 1 −
𝑖
𝑝 𝑖
2

16. Feature importance
16

17. It is end of the beginning
• Easy to interpret features for physicians
• Analyzing other vital signs
• Monitoring treatments (Injections, transitions, …)
• Other intensive care unites
• Extension to other wards of hospital
• Real time decision making
• …
17

18. Steps to explainable
clinical decision support systems
• Easy to interpret features from different signs such as heart rate variability, electrodermal activity, body
movement, and temperature
• Meeting with physicians
• Tracking subjects via different activities
• Focus on time domain features vs frequency ones
• Using interpretable classifier vs black-boxes such as DNN(CNN, LSTM, …)
• Publishing code and data
• …..
18

19. Slides, Paper, and code of this project are
accessible via
http://knoesis.org/Reza
Thank you for your attention!

20. Extra
• Gini index
𝐺𝐷𝐼 = 1 −
𝑖
(𝑝 𝑖 )2
• The risk of splitting
𝑅𝑖𝑠𝑘 𝑥 = 𝐺𝐷𝐼 𝑥 . 𝑃𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦(𝑥)
• The node probability is defined as the number of records reaching the node,
divided by the total number of records.
• We utilize a resampling method called adaptive semi-unsupervised
weighted oversampling (A-SUWO)
20

21. Missing Data Type:
1. Missing completely at random (MCAR)
2. Missing at random (MAR)
3. Missing not at random (MNAR)
Missing Data Handling methods:
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.
4. Predicted by using a learning algorithm, such as
Multiple Imputation or EMImputation
Imbalance data Handling:
1. Re-sampling : under sampling and
oversampling.
2. Making the classifier 'cost sensitive'
3. Hybrid method
4. One class classifiers
Data mining models issues
Interpretability:
1. Deep learning
2. Ensemble methods

22. Signal based features
The averaged power of a finite discrete-time
signal is defined as the mean of the signal’s energy
𝑃 =
𝐸
𝑛2 − 𝑛1 + 1
=
1
𝑛2 − 𝑛1 + 1
𝑛1
𝑛2
𝑆[𝑛]2
- The signal power is computed by taking the integral of the power spectral
density (PSD)
𝑃 =
∆𝑇
𝑁
𝑛=0
𝑁−1
𝑆[𝑛]𝑒−𝑖2𝜋𝜌
22