1. Intelligent Database Systems Lab Presenter : CHANG, SHIH-JIE Authors : Ya-Han Hu, Fan Wu a, Chia-Lun Lo, Chun-Tien Tai b 2012.AIM. Predicting warfarin dosage from clinical data: A supervised learning approach

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab Motivation  Physicians use computerized dosing nomograms of warfarin as reference.It merely consider age and INR values not enough for dose adjustment.

4. Intelligent Database Systems Lab Objectives Build a warfarin dosage prediction model utilizing a number of supervised learning techniques to help dose adjustment.

5. Intelligent Database Systems Lab Warfarin

6. Intelligent Database Systems Lab Prediction model for warfarin dosing- Single classifiers (1) KNN (2) SVR Given a set of training instances xi : input vector yi : actual output of xi a regression function ε-SVR can be formulated regression hyperplane

7. Intelligent Database Systems Lab Methodology - Single classifiers (3) M5 (model-tree-based regression algorithm) use standard deviation reduction Tree-building : specific node standard deviation of the class values of all instances in a child-node Nt,i,

8. Intelligent Database Systems Lab Methodology - M5 error term tree-pruning

9. Intelligent Database Systems Lab Methodology – MLP (4) MLP

10. Intelligent Database Systems Lab Methodology – Classifier ensemble Voting (weight) Bagged Voting method    Decide the estimated output by combining the results of different classifiers.

11. Intelligent Database Systems Lab Experiments – Data preparation Collected 587 clinical cases (INR value 1~3) Drug-to-drug interaction (DDI) 424 163 Use Bagging 496

12. Intelligent Database Systems Lab Experiments – Performance measures

13. Experiments – Evaluation results

14. Intelligent Database Systems Lab Experiments – The average of evaluation results

15. Intelligent Database Systems Lab Conclusions –The investigated models can not only facilitate clinicians in dosage decision-making, but also help reduce patient risk from adverse drug events.

16. Intelligent Database Systems Lab Comments Advantages –More accurate. Applications –Warfarin dosage prediction.