PMID- 24136011
OWN - NLM
STAT- MEDLINE
DCOM- 20150416
LR  - 20211203
IS  - 2511-705X (Electronic)
IS  - 0026-1270 (Linking)
VI  - 53
IP  - 1
DP  - 2014
TI  - Improvement of adequate use of warfarin for the elderly using decision tree-based 
      approaches.
PG  - 47-53
LID - 10.3414/ME13-01-0027 [doi]
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.
FAU - Liu, K E
AU  - Liu KE
FAU - Lo, C-L
AU  - Lo CL
FAU - Hu, Y-H
AU  - Hu YH
AD  - 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.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20131018
PL  - Germany
TA  - Methods Inf Med
JT  - Methods of information in medicine
JID - 0210453
RN  - 0 (Anticoagulants)
RN  - 5Q7ZVV76EI (Warfarin)
SB  - IM
MH  - Aged
MH  - Aged, 80 and over
MH  - Algorithms
MH  - Anticoagulants/administration & dosage/*adverse effects/*therapeutic use
MH  - Artificial Intelligence
MH  - Body Weight
MH  - Clinical Laboratory Information Systems
MH  - Comorbidity
MH  - Cross-Cultural Comparison
MH  - *Decision Trees
MH  - Dose-Response Relationship, Drug
MH  - Drug Interactions
MH  - Ethnicity
MH  - Female
MH  - Heart Failure/diagnosis
MH  - Humans
MH  - Male
MH  - Medical History Taking
MH  - Middle Aged
MH  - *Quality Improvement
MH  - Risk Factors
MH  - Taiwan
MH  - Thyrotoxicosis/diagnosis
MH  - Warfarin/administration & dosage/*adverse effects/*therapeutic use
OTO - NOTNLM
OT  - Warfarin
OT  - anticoagulant
OT  - decision support techniques
OT  - decision trees
OT  - health services for the elderly
EDAT- 2013/10/19 06:00
MHDA- 2015/04/17 06:00
CRDT- 2013/10/19 06:00
PHST- 2013/03/06 00:00 [received]
PHST- 2013/09/16 00:00 [accepted]
PHST- 2013/10/19 06:00 [entrez]
PHST- 2013/10/19 06:00 [pubmed]
PHST- 2015/04/17 06:00 [medline]
AID - 13-01-0027 [pii]
AID - 10.3414/ME13-01-0027 [doi]
PST - ppublish
SO  - Methods Inf Med. 2014;53(1):47-53. doi: 10.3414/ME13-01-0027. Epub 2013 Oct 18.