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

Liu, K. E. ; Lo, C.-L. ; Hu, Y.-H.

Methods of information in medicine, 2014, Vol.53 (1), p.47-53 [Periódico revisado por pares]

Germany: Schattauer Verlag für Medizin und Naturwissenschaften

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Título:
Improvement of Adequate Use of Warfarin for the Elderly Using Decision Tree-based Approaches
Autor: Liu, K. E. ; Lo, C.-L. ; Hu, Y.-H.
Assuntos: Aged ; Aged, 80 and over ; Algorithms ; anticoagulant ; Anticoagulants - administration & dosage ; Anticoagulants - adverse effects ; Anticoagulants - therapeutic use ; Artificial Intelligence ; Body Weight ; Clinical Laboratory Information Systems ; Comorbidity ; Cross-Cultural Comparison ; Decision Support Techniques ; Decision Trees ; Dose-Response Relationship, Drug ; Drug Interactions ; Ethnic Groups ; Female ; health services for the elderly ; Heart Failure - diagnosis ; Humans ; Male ; Medical History Taking ; Middle Aged ; Original Articles ; Quality Improvement ; Risk Factors ; Taiwan ; Thyrotoxicosis - diagnosis ; warfarin ; Warfarin - administration & dosage ; Warfarin - adverse effects ; Warfarin - therapeutic use
É parte de: Methods of information in medicine, 2014, Vol.53 (1), p.47-53
Descrição: Objectives: 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.
Editor: Germany: Schattauer Verlag für Medizin und Naturwissenschaften
Idioma: Alemão;Inglês

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

Liu, K. E. ; Lo, C.-L. ; Hu, Y.-H.

Germany: Schattauer Verlag für Medizin und Naturwissenschaften

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