Health Informatics J - Statistical classification of drug incidents due to look-alike sound-alike mix-ups.

Tópicos

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Resumo

It has been recognised that medication names that look or sound similar are a cause of medication errors. This study builds statistical classifiers for identifying medication incidents due to look-alike sound-alike mix-ups. A total of 227 patient safety incident advisories related to medication were obtained from the Canadian Patient Safety Institute's Global Patient Safety Alerts system. Eight feature selection strategies based on frequent terms, frequent drug terms and constituent terms were performed. Statistical text classifiers based on logistic regression, support vector machines with linear, polynomial, radial-basis and sigmoid kernels and decision tree were trained and tested. The models developed achieved an average accuracy of above 0.8 across all the model settings. The receiver operating characteristic curves indicated the classifiers performed reasonably well. The results obtained in this study suggest that statistical text classification can be a feasible method for identifying medication incidents due to look-alike sound-alike mix-ups based on a database of advisories from Global Patient Safety Alerts.

Resumo Limpo

recognis medic name look sound similar caus medic error studi build statist classifi identifi medic incid due lookalik soundalik mixup total patient safeti incid advisori relat medic obtain canadian patient safeti institut global patient safeti alert system eight featur select strategi base frequent term frequent drug term constitu term perform statist text classifi base logist regress support vector machin linear polynomi radialbasi sigmoid kernel decis tree train test model develop achiev averag accuraci across model set receiv oper characterist curv indic classifi perform reason well result obtain studi suggest statist text classif can feasibl method identifi medic incid due lookalik soundalik mixup base databas advisori global patient safeti alert

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