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Medication error has been a severe patient safety event in the United States. Medication error reports collected by Patient Safety Organizations provide an opportunity to analyze and learn from previous cases. However, the current process of analyzing the reports is labor-intensive and time-consuming. To improve the efficiency, we used automated text classification techniques to develop a pipeline for medication error report pre-analysis. The pipeline was proven functional in two tasks, i.e., identifying the non-preventable adverse drug events from medication error reports, and identifying the error originated stages during the medication distribution process. The proposed pipeline holds promise in helping clinicians understand the nature of medication error in an error report, and locate the potential root causes of the error, which could further facilitate to reduce medication errors in healthcare settings.
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