Improvement of Adequate Use of Warfarin for the Elderly Using Decision Tree-based Approaches

Autor: Ya Han Hu, C. L. Lo, Kang Ernest Liu
Rok vydání: 2014
Předmět:
Cross-Cultural Comparison
Male
0301 basic medicine
Decision support system
Boosting (machine learning)
020205 medical informatics
Taiwan
Decision tree
Health Informatics
Comorbidity
02 engineering and technology
computer.software_genre
03 medical and health sciences
Health Information Management
Artificial Intelligence
Risk Factors
Ethnicity
0202 electrical engineering
electronic engineering
information engineering
medicine
Information system
Humans
Drug Interactions
AdaBoost
Predicting performance
Medical History Taking
Aged
Aged
80 and over
Heart Failure
Advanced and Specialized Nursing
Dose-Response Relationship
Drug
business.industry
Body Weight
Decision Trees
Warfarin
Anticoagulants
Middle Aged
Quality Improvement
Thyrotoxicosis
030104 developmental biology
Female
Data mining
Clinical Laboratory Information Systems
business
computer
Algorithms
Predictive modelling
medicine.drug
Zdroj: Methods of Information in Medicine. 53:47-53
ISSN: 2511-705X
0026-1270
DOI: 10.3414/me13-01-0027
Popis: SummaryObjectives: 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.Conclusion: 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.
Databáze: OpenAIRE
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