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Predicting warfarin dosage from clinical data: A supervised learning approach

Predicting warfarin dosage from clinical data: A supervised learning approach. Presenter : CHANG, SHIH-JIE Authors : Ya -Han Hu , Fan Wu a, Chia-Lun Lo, Chun- Tien Tai b 2012.AIM. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Predicting warfarin dosage from clinical data: A supervised learning approach

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  1. Predicting warfarin dosage from clinical data: A supervised learning approach Presenter : CHANG, SHIH-JIE Authors : Ya-Han Hu, Fan Wu a, Chia-Lun Lo, Chun-Tien Tai b2012.AIM.

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • Physicians use computerized dosing nomogramsof warfarinas reference .It merely consider age and INR values not enough for dose adjustment.

  4. Objectives • Build a warfarin dosagepredictionmodel utilizing a number of supervised learning techniques to help dose adjustment.

  5. Warfarin

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

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

  8. Methodology - M5 tree-pruning error term

  9. Methodology – MLP (4) MLP

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

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

  12. Experiments – Performance measures

  13. Experiments – Evaluation results

  14. Experiments – The average of evaluation results

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

  16. Comments • Advantages • More accurate. • Applications • Warfarin dosage prediction.

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