TY - JOUR
T1 - Improvement of adequate use of warfarin for the elderly using decision tree-based approaches
AU - Liu, K. E.
AU - Lo, C. L.
AU - Hu, Ya Han
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Anticoagulant
KW - Decision support techniques
KW - Decision trees
KW - Health services for the elderly
KW - Warfarin
UR - http://www.scopus.com/inward/record.url?scp=84892397445&partnerID=8YFLogxK
U2 - 10.3414/ME13-01-0027
DO - 10.3414/ME13-01-0027
M3 - 期刊論文
C2 - 24136011
AN - SCOPUS:84892397445
SN - 0026-1270
VL - 53
SP - 47
EP - 53
JO - Methods of Information in Medicine
JF - Methods of Information in Medicine
IS - 1
ER -