2016
DOI: 10.1002/pds.3976
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An automated technique to identify potential inappropriate traditional Chinese medicine (TCM) prescriptions

Abstract: We successfully applied the AOP model to automatically identify potential inappropriate TCM prescriptions. This model could be a potential TCM clinical decision support system in order to improve drug safety and quality of care.

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Cited by 8 publications
(9 citation statements)
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“…However, although some significant challenges of TCM have currently faced from both the application of modern medicine and social development, the clinical practice of TCM still accounts for about 20% of all health care delivered in China [ 8 ]. Additionally, much attention has been captured on patient safety and quality of care to TCM users [ 7 ].…”
Section: Introductionmentioning
confidence: 99%
“…However, although some significant challenges of TCM have currently faced from both the application of modern medicine and social development, the clinical practice of TCM still accounts for about 20% of all health care delivered in China [ 8 ]. Additionally, much attention has been captured on patient safety and quality of care to TCM users [ 7 ].…”
Section: Introductionmentioning
confidence: 99%
“…However, data mining techniques are customizable and can identify inappropriate prescriptions. For example, association rule mining has been used to find inappropriate prescriptions by calculating the co-occurrence of medications and diseases, resulting in a sensitivity of 75.9% and a specificity of 89.5% [ 42 , 43 ]. These methods however do have disadvantages, including inefficiency in the generation of candidate item sets because they require vast data sources and the frequent scanning of databases.…”
Section: Discussionmentioning
confidence: 99%
“…For model evaluation, considering the sensitivity and specificity (71.5% and 68.8%, respectively) of the Apriori algorithm in previous work [ 42 , 43 ], we set both the expected sensitivity and specificity at 80%. By setting a significance level of .05 and an allowable error of 0.05, we needed at least 14,471 prescriptions given 1.7% prevalence [ 44 ] of prescription misuse according to equation 1.…”
Section: Methodsmentioning
confidence: 99%
“…Reference standards in herbal medicine 3 11.11 (24,27,37) Regulation of herbal medicinal products 8 30 (13,24,25,28,30,31,43,45) Toxicity evaluation in herbal products 7 26 (23,25,27,28,30,38,46) Quality control of herbal medicine 11 40.75 (4,7,9,11,27,(31)(32)(33)39,42,44) Efficacy in herbal medicine 9 33.33 (3,4,11,13,25,29,33,39,40) Adverse effects of herbal medicine 7 26 (7,32,36,38,41,43,46) Safety in herbal medicine 12 44.45 (3,11,13,…”
Section: Factors Number = 27 Percentage (%) Referencesmentioning
confidence: 99%