1 / 16

Enhancing Warfarin Dosage Prediction with Supervised Learning

This study aims to build a warfarin dosage prediction model using various supervised learning techniques to assist in dose adjustment for better patient outcomes. Methods include KNN, SVR, M5, MLP, and classifier ensemble. Experiments with 587 clinical cases showed the models can aid clinicians in dosage decisions and reduce patient risks from adverse drug events. Advantages include accuracy and wide applications in warfarin dosage prediction.

eithne
Télécharger la présentation

Enhancing Warfarin Dosage Prediction with Supervised Learning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related