Predicting Risk of Re-hospitalization for Congestive Heart Failure Patients(in collaboration with )

Jayshree AgarwalSenjuti Basu Roy,

Ankur Teredesai, Si-Chi Chin, David Hazel, Kiyana, Mehrdad, (UWT)

Paul Amoroso, Yoshi Williams, Dr. Lester Reed, Sheila, Eric Johnson (MHS)

Motivation

Congestive Heart

Failure(CHF)

Many hospitalizations

readmissions

19.6% patients readmitted

within 30 days [Jencks et al.

2009]

31.1% patients readmitted

within 60 days [Jencks et al.

2009]

LOW Readmission rate = HIGH

quality of care by hospital

No reimbursement for readmission within 30 days

$$$COST - 2004 unplanned re-admits = $17.4 billion [Jencks et

al. 2009]

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MHS - UWT Web and Data Science collaboration objectives

Predict the RISK of Readmission for CHF patientsReduce the Readmission rate and cost Improve patient satisfaction and quality of careAppropriate pre-discharge and post-discharge planningProper resource utilization

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Problem

Develop models that can predict risk of readmission for CHF patients within 30 days after discharge 60 days after discharge

The readmission may happen for other reasons in addition to CHF

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Overall Approach

How to solve the problem?– Apply predictive data mining techniques such as,

classificationWhat do these predictive mining techniques

require?– Data in homogeneous format

• Information Extraction, Integration, and data preparation• Prepare labeled dataset to train the model; used later on

for testing.

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Our ChallengesBuilding domain knowledge– Which variables to consider?– How to merge and unify them in a homogeneous format

(information extraction and integration)– How to understand the relative importance of the

variables in the prediction task?How to prepare data?– Class label generation– Noisy real world data (missing values, inconsistencies,

etc.)– Serious skew in the dataset

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Solution

Building Predictive Classification Models

Data Understanding

Data Preprocessing

Modeling

Evaluation

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Data Understanding

Collect initial data Acquire Domain knowledge

Describe and explore dataset

Create data visualization

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Building Predictive Classification Models

Data Understanding

Data Preprocessing

Modeling

Evaluation

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Data Preprocessing

Define class label Attribute selection

Data Integration

Removal of incomplete data

Finding Eligible CHF admissions

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Eligible CHF admissions and Generating Class Labels

All CHF Admissions

Eligible CHF Admissions

In hospital deaths removed

Is there any readmission

within x days of discharge?

The class label is assigned as 1

The class label is assigned as 0

YESNO

X=30 X=60

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Attribute selection

Yale Model [Krumholz et al]

-Socio-Demographic variable(2)

-Comorbidities(35)

“Baseline”

Additional predictor variables identified by us

(14)

“New”

“Correlated”“All”

Chi-square correlation test

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Data Extraction

Labeled data

Patient details

Primary and Secondary diagnosis

Lab measurement

Administrative data

Data used for training the Models

Data

Incomplete data removed

Table Joins

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Data Distribution

30 days time frame 60 days time frame

Readmissions0

2000

4000

6000

8000

10000

12000

ReadmitNo Readmit

Readmissions0

2000

4000

6000

8000

10000

12000

ReadmitNo Readmit

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Building Predictive Classification Models

Data Understanding

Data Preprocessing

Modeling

Evaluation

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Modeling

• Logistic regression• Naïve Bayes classifier• Support Vector Machine

Balancing imbalanced data by under-sampling and over

sampling

Selecting modeling technique for Binary

Classification

Building prediction models

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Logistic Regression Model

P (P

roba

bilit

y of

Y)

Z ------>

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Naïve Bayesian Classification

Statistical Classifier performs probabilistic prediction based on Bayes Theorem

Assumes that the attributes are conditionally independent

Given a data tuple X and m classes Predicts X belongs to only if is highest among all the

for all the m classes

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Support Vector Machine

A method of classification for both linear and non linear data

Searches for optimal separating hyperplane separating the two classes

Building Predictive Classification Models

Data Understanding

Data Preprocessing

Modeling

Evaluation

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Performance Evaluation Metrics

Precision – percentage of tuples labeled as positive are actually positive = TP/TP+FP

Recall – measures the percentage of positive tuples that are labeled positive = TP/TP+FN

Accuracy – percentage of tuples correctly classified = (TP+TN)/P+N ROC curves and area under the curve (AUC) – Shows the trade-off

between true positive rate and false positive rate.

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Evaluation

• Predictive models are assessed using 10 fold cross validation

• The performance is compared using different evaluation metrics mentioned previously

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RESULTS

Logistic Regression for 30 days

Area Under the Curve (AUC) Recall

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Logistic regression for 60 days

Area Under the Curve (AUC) Recall

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Naïve Bayes classifier for 30 days

Attribute Set0.56

0.57

0.58

0.59

0.6

0.61

0.62

0.63

0.64

BaselineNewAllCorrelated

Area Under the Curve (AUC)

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Support Vector Machine for 30 days

Attribute Set0.58

0.59

0.6

0.61

0.62

0.63

0.64

BaselineNewAllCorrelated

Area Under the Curve (AUC)

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Conclusion and Discussion

It is one of the difficult problem to solveFeature selection gives the best results. With data balancing recall of the model improves

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Future Work

Investigate other classifier techniques like ensemble methods, neural networks

To explore additional features and study their relevance

To employ other feature selection techniquesTo device a method to impute missing valuesDeploying the predictive models

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Acknowledgement

Multicare health System (MHS) and Dr. Lester Reed for giving us this opportunity

Data architects and domain experts in MHS for their inputs

Professors Dr. Ankur Teredesai and Dr. Senjuti Basu Roy for their guidance

Other team members in UWT for their support

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References

S. F. Jencks, M. V. Williams, and E. A. Coleman, “Rehospitalizations among Patients in the Medicare Fee-for-Service Program,” New England Journal of Medicine, vol. 360, no. 14, pp. 1418–1428, 2009.

J. Han and M. Kamber, Data mining: concepts and techniques. Morgan Kaufmann, 2006

H. M. Krumholz, S. L. T. Normand, P. S. Keenan, Z. Q. Lin, E. E. Drye, K. R. Bhat, Y. F. Wang, J. S. Ross, J. D. Schuur, and B. D. Stauffer, Hospital 30-day heart failure readmission measure methodology. Report prepared for the Centers for Medicare & Medicaid Services.

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Questions