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dc.contributor.authorPakpoom Wongyikulen_US
dc.contributor.authorNuttamon Thongyoten_US
dc.contributor.authorPannika Tantrakoolcharoenen_US
dc.contributor.authorPusit Seephuengen_US
dc.contributor.authorPiyapong Khumrinen_US
dc.description.abstractPrescription errors in high alert drugs (HAD), a group of drugs that have a high risk of complications and potential negative consequences, are a major and serious problem in medicine. Standardized hospital interventions, protocols, or guidelines were implemented to reduce the errors but were not found to be highly effective. Machine learning driven clinical decision support systems (CDSS) show a potential solution to address this problem. We developed a HAD screening protocol with a machine learning model using Gradient Boosting Classifier and screening parameters to identify the events of HAD prescription errors from the drug prescriptions of out and inpatients at Maharaj Nakhon Chiang Mai hospital in 2018. The machine learning algorithm was able to screen drug prescription events with a risk of HAD inappropriate use and identify over 98% of actual HAD mismatches in the test set and 99% in the evaluation set. This study demonstrates that machine learning plays an important role and has potential benefit to screen and reduce errors in HAD prescriptions.en_US
dc.titleHigh alert drugs screening using gradient boosting classifieren_US
article.title.sourcetitleScientific Reportsen_US
article.volume11en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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