http://signal.nyc/

1. Sina Kashuk
Insight Fellow

2. Question
How long a patient would/should stay in hospital?
Image Source: https://www.woodcountyhospital.org/files/siteimages/patientandvisitorinformation/happypatient-banner.jpg

3. Data Source
• Hospital Inpatient Discharges
• SPARCS
(Statewide Planning and
Research Cooperative System)
• From 2009 - 2013
• 12M+ De-Identified Records
• Provided by New York State
Department of Health
Features:
1.Gender
2.Age Group
3.Race
4.Admit Day of Week
5.Type of Admission
6.Patient Disposition
7.Severity of Illness
8.Risk of Mortality
9.CCS Diagnosis
Outcome: Length of Stay
}

4. Algorithm
Random Forest
Regression
80%-20%
Train-Test
Feature
Importance
Pre-Processing
(Square-Root
Transformation)

5. Acute Cerebrovascular Disease (CVD)
Error Histogram
FeatureImportance
Features
Effect on Length of Stay
f (Predicted) - y (Actual)
R2=0.424
250,000 Records
Average Length of Stay 7.7 Days
Adm
ission
[elective]
Race
[W
hite]
Risk
of
M
ortality
Surgical
Severity
ofIllness

6. Tree Visualization (Acute CVD)

7. Take Away
Helping hospital administrative to balance the load
by predicting how long a patient stay in hospital

8. About me
Sina Kashuk

9. Histogram - Length of Stay

10. Tree Visualization (#10)

11. Tree Visualization (#20)

12. Feature Importance

13. Squared-root Transformation of Length of Stay
Before
After
day
√day

14. Diagnoses and the Length of Stay

15. Effect of Age on Length of Stay

16. Effect of Severity of Illness on Length of Stay

17. Effect of Admission Day on Length of Stay

18. Record Proportions of Different Admission
Types for 10 Diagnoses

19. Effect of Day of Admission and Proportion of
Alive vs. Expired

20. Appendicitis

21. Colon Cancer

22. Intracranial Injury

23. Parallel Coordinate Visualization
Female
Male
30 to 49
Moderate
Major
Minor
0 to 17
18 to 29
50 to 69
70 or Older
Extreme
60 days 60 days

24. Other Models
• Naive Bayes
• Random Forests
• Gradient Boosting
• Ordinary Least Squares
• Random Forests
• Gradient Boosting
Weekly
Classification
Daily
Regression