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Predicting Risk of Re-hospitalization for Congestive Heart Failure Patients (in collaboration with ) Jayshree Agarwal Senjuti Basu Roy, Ankur Teredesai,

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Presentation on theme: "Predicting Risk of Re-hospitalization for Congestive Heart Failure Patients (in collaboration with ) Jayshree Agarwal Senjuti Basu Roy, Ankur Teredesai,"— Presentation transcript:

1 Predicting Risk of Re-hospitalization for Congestive Heart Failure Patients (in collaboration with ) Jayshree Agarwal Senjuti Basu Roy, Ankur Teredesai, Si-Chi Chin, David Hazel, Kiyana, Mehrdad, (UWT) Paul Amoroso, Yoshi Williams, Dr. Lester Reed, Sheila, Eric Johnson (MHS)

2 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 unplanned re-admits = $17.4 billion [Jencks et al. 2009] 2

3 MHS - UWT Web and Data Science collaboration objectives  Predict the RISK of Readmission for CHF patients  Reduce the Readmission rate and cost  Improve patient satisfaction and quality of care  Appropriate pre-discharge and post-discharge planning  Proper resource utilization 3

4 Benefits of predicting Risk or Readmission  Proper resource utilization  Improvement in quality of care  Targeted interventions can be planned  Proper pre-discharge and post-discharge plans can be made  Reduction in cost  Medicare expenditure on potentially preventable re- hospitalization is around $12 billion [Jencks et al. 2009] 4

5 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 5

6 Overall Approach  How to solve the problem? – Apply predictive data mining techniques such as, classification  What 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. 6

7 Our Challenges  Building 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 7

8 Solution 8

9 Building Predictive Classification Models Data Understanding Data Preprocessing Modeling Evaluation 9

10 Data Understanding Collect initial data Acquire Domain knowledge Describe and explore dataset Create data visualization 10

11 Building Predictive Classification Models Data Understanding Data Preprocessing Modeling Evaluation 11

12 Data Preprocessing Define class label Attribute selection Data Integration Removal of incomplete data Finding Eligible CHF admissions 12

13 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 YES NO X=30 X=60 13

14 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 14

15 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 15

16 Data Distribution  30 days time frame  60 days time frame 16

17 Building Predictive Classification Models Data Understanding Data Preprocessing Modeling Evaluation 17

18 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 18

19 Logistic Regression Model P (Probability of Y) Z > 19

20 Naïve Bayesian Classification 20

21 Support Vector Machine  A method of classification for both linear and non linear data  Searches for optimal separating hyperplane separating the two classes 21

22 Building Predictive Classification Models Data Understanding Data Preprocessing Modeling Evaluation 22

23 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. 23

24 Baseline Model  Hospital 30 day Heart Failure Readmission Measure submitted by Yale University [Krumholz et al. ]  Used Hierarchical Logistic Regression Model  The Area under the Curve (AUC) is

25 Evaluation Predictive models are assessed using 10 fold cross validation The performance is compared using different evaluation metrics mentioned previously 25

26 RESULTS

27 Logistic Regression for 30 days Area Under the Curve (AUC) Recall 27

28 Logistic regression for 60 days Area Under the Curve (AUC) Recall 28

29 Naïve Bayes classifier for 30 days 29 Area Under the Curve (AUC)

30 Support Vector Machine for 30 days 30 Area Under the Curve (AUC)

31 Results of Logistic regression AttributePrecisionRecallAccuracyF1 scoreAUC 30 days time frame A A A A days time frame A A A A

32 Results of Naïve Bayes AttributePrecisionRecallAccuracyF1 scoreAUC 30 days time frame A A A A days time frame A A A A

33 Results of Support Vector Machine AttributePrecisionRecallAccuracyF1 scoreAUC 30 day time frame A A A A day time frame A A A A

34 Comparison of AUC of different Models Baseline Model Logistic Regression Naïve Bayesian Support vector machine 30 days timeframe days timeframe

35 Conclusion and Discussion  It is one of the difficult problem to solve  Feature selection gives the best results.  With data balancing recall of the model improves 35

36 Future Work  Investigate other classifier techniques like ensemble methods, neural networks  To explore additional features and study their relevance  To employ other feature selection techniques  To device a method to impute missing values  Deploying the predictive models 36

37 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 37

38 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,  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. 38

39 Questions 39


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