1. Cost of Medical Care
in Older Adults with Chronic Conditions
Randal S. Goomer, PhD
Data Science
DS-SF-29
General Assembly
San Francisco, 1/25/2017
Randal S. Goomer, PhD

2. DataSet
Medicare 2010 Patient records
Expunged of all personal or identifiable information (33M patient profiles)
Provided by
CMS
As: 2010 Chronic Conditions Public Use File (PUF)
(2010 CMS CC PUF)
Patient Age
Categories: 1 – 6
1. 62-64
2. 65-69
3. 70-74
4. 75-79
5. 80-85
6. 85----
Therapy Behavior:
• Number of Out-
Patient Visits
• Number of In-
Patient admits
• Medicare Part A,
B, C, D, E
payments
Chronic Conditions  CC
• Alzhiemers
• Cancer
• CHF
• Diabetes
• ChrKidneyDisease
• Stroke
• Osteoporosis
• Depression
• COPD
• Ischemic Heart Condition
• Stroke
• Arthritis
Patient Gender
Cost
(Payouts by Medicare)
Randal S. Goomer, PhD

3. Dataset Includes a 4-page Dictionary of Terms
(Sample: page 1 of 4)
Randal S. Goomer, PhD

4. Can we predict costs based on patient profile or behavior?
BI Questions
• Does the type of chronic conditions (CC) impact Costs?
• Does age or gender impact cost?
• Does patient behavior such as accessing OP facilities vs. IP
admits influence costs?
• Which behavior costs more of less?
• Which CC cost more or less?
Ho: <<we cannot predict cost from patient profile and behavior>>
H1: <<Patient profile and behavior can predict cost>>
Randal S. Goomer, PhD

5. ML models
• Data Munging (pd, np)
• Random Forest
• RF optimized by bootstrapping (with replacement)
• and OOB error testing against ROC/AUC
• OLS (p-val, r-squared, coeff., predict accuracy)
• Logit regression (coeff_, curve_fitting, Predict Prob.)
• K-means classification (with K_fold-grid_CV optimization)
• Visualizations (Seaborn, MatplotLib)
Randal S. Goomer, PhD

6. Heatmap:
Order of importance
w.r.t. ‘payout’ or Cost:
- ip_admit = (hospitalization)
- op_visits = (offices/op-clinics)
- CC_CHF = (Chronic Heart Failure)
- CC_CANCER = (Cancer patients)
- CC_ISCHMCHT = (Ischemic Heart Dis)
- CC_CHRNKIDN = (Chronic kidney Dis)
After Detailed Data ‘Munging’, Heatmap was produced
Heatmap finds hidden correlations
Chronic Kidney Dis.
Osteoporosis
Randal S. Goomer, PhD

7. Cost v. In-Patient Admit
EDA: Costs Rise Quickly for number of In-Patient Admits
Payout >>
In-PatientAdmits
Randal S. Goomer, PhD

8. EDA: Costs Plateau out quickly for number of Out-Patient visits
Out-PatientVisits
Payout >>
Cost v. Out-Patient Visit
Randal S. Goomer, PhD

9. Patients with Chronic Heart Failure (CHF) by Age v. Cost
Patient age and Chronic Condition contributes to Cost
Payout>>
(62 yrs  85+ yrs) (62 yrs  85+ yrs)
CHF = TrueCHF = False
Randal S. Goomer, PhD

10. Age v. IP Admit and OP visitsIn-PatientAdmit
In-Patient Admits
Out-PatientVisits(62 yrs  85+ yrs) (62 yrs  85+ yrs)Randal S. Goomer, PhD

11. Payout v. Out-Patient visits
Lin-Reg
Outpatient VisitsRandal S. Goomer, PhD

12. Payout v. IP Admit
Lin-Reg
Payout
IP AdmitRandal S. Goomer, PhD

13. Ip_admit significantly affects cost
based on p value but not linearly
(r2 = 0.535)
Randal S. Goomer, PhD

14. Age is a predictor of
cost but does not have
a linear relationship
due to low r2 value
Randal S. Goomer, PhD

15. When you have cancer
then age is no longer a
predictor of cost
Randal S. Goomer, PhD

16. Randal S. Goomer, PhD

17. IP Admit for Osteoporosis
Not Needed Anymore
(Because of ready availability of long acting drugs)Randal S. Goomer, PhD

18. IP Admit for Chronic Heart Failure (CHF)
IP Admit
No Need
Still Needed
Randal S. Goomer, PhD

19. IP Admit for Chronic Heart Failure (CHF)
IP Admit Still Needed
Very much
Logistic regression: Log predicted probability
Randal S. Goomer, PhD

20. CART = Classification and Regression Tree
Randal S. Goomer, PhD

21. Random Forest 1
Randal S. Goomer, PhD

22. Random Forest 1
Feature Importance
Compared to our heatmap from above
Randal S. Goomer, PhD

23. Random Forest: Trained n_estimators using bootstrap from 30 to
2,000 Trees using AUC as output; Optimized at 1,000 trees
Randal S. Goomer, PhD

24. #### the above shows stats when using all features (auto and
none), sqrt of features numbers (100 features == 10 used),
90%, or 20%.
Random Forest: optimized max-features options, using bootstrap from
using AUC as output; ‘auto’, sqrt, log2, 0.9, 0.2 used.
Randal S. Goomer, PhD

25. Random Forest: optimized min-sample option, using AUC as output.
Randal S. Goomer, PhD

26. K-means Clustering with Silhouette Score
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300, n_clusters=10, n_init=10, n_jobs=1,
precompute_distances='auto', random_state=42, tol=0.0001, verbose=0) , here, white plus signs represent the centroids
Randal S. Goomer, PhD

27. K-means Clusters with grid_cv
Randal S. Goomer, PhD

28. Randal S. Goomer, PhD

29. Future:
Ensemble technique using
pipeline that includes RF,
logReg and GBM
for manuscript preparation
and publication.
Randal S. Goomer, PhD