title: Improvement of adequate use of warfarin for the elderly using decision tree-based approaches.
abstract: OBJECTIVE
  Due to the narrow therapeutic range and high drug-to-drug interactions (DDIs), improving the adequate use of warfarin for the elderly is crucial in clinical practice. This study examines whether the effectiveness of using warfarin among elderly inpatients can be improved when machine learning techniques and data from the laboratory information system are incorporated.
  
  
  METHODS
  Having employed 288 validated clinical cases in the DDI group and 89 cases in the non-DDI group, we evaluate the prediction performance of seven classification techniques, with and without an Adaptive Boosting (AdaBoost) algorithm. Measures including accuracy, sensitivity, specificity and area under the curve are used to evaluate model performance.
  
  
  RESULTS
  Decision tree-based classifiers outperform other investigated classifiers in all evaluation measures. The classifiers supplemented with AdaBoost can generally improve the performance. In addition, weight, congestive heart failure, and gender are among the top three critical variables affecting prediction accuracy for the non-DDI group, while age, ALT, and warfarin doses are the most influential factors for the DDI group.
  
  
  CONCLUSIONS
  Medical decision support systems incorporating decision tree-based approaches improve predicting performance and thus may serve as a supplementary tool in clinical practice. Information from laboratory tests and inpatients' history should not be ignored because related variables are shown to be decisive in our prediction models, especially when the DDIs exist.
authors.forename: K E
authors.initials: KE
authors.lastname: Liu
authors.forename: C-L
authors.initials: CL
authors.lastname: Lo
authors.affiliation: Ya-Han Hu, Department of Information Management and Graduate Institute of Healthcare Information Management, National Chung Cheng University, 168 University Road, Min-Hsiung Chia-Yi 62102, Taiwan, E-mail: yahan.hu@mis.ccu.edu.tw.
authors.forename: Y-H
authors.initials: YH
authors.lastname: Hu
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000925:Anticoagulants / Q000008:administration & dosage / Q000009:adverse effects* / Q000627:therapeutic use*; D001185:Artificial Intelligence; D001835:Body Weight; D002984:Clinical Laboratory Information Systems; D015897:Comorbidity; D003431:Cross-Cultural Comparison; D003663:Decision Trees; D004305:Dose-Response Relationship, Drug; D004347:Drug Interactions; D005006:Ethnicity; D005260:Female; D006333:Heart Failure / Q000175:diagnosis; D006801:Humans; D008297:Male; D008487:Medical History Taking; D008875:Middle Aged; D058996:Quality Improvement; D012307:Risk Factors; D013624:Taiwan; D013971:Thyrotoxicosis / Q000175:diagnosis; D014859:Warfarin / Q000008:administration & dosage / Q000009:adverse effects* / Q000627:therapeutic use*
chemical_list: D000925:Anticoagulants; D014859:Warfarin
keywords: Warfarin; anticoagulant; decision support techniques; decision trees; health services for the elderly