An automated pipeline for analyzing medication event reports in clinical settings.

ABSTRACT:

Background

Medication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medication event reports is ineffective and requires heavy workloads for clinicians. An automated pipeline is proposed to help clinicians deal with the accumulated reports, extract valuable information and generate feedback from the reports. Thus, the strategy of medication event prevention can be further developed based on the lessons learned.

Methods

In order to build the automated pipeline, four classic machine learning classifiers (i.e., support vector machine, Naïve Bayes, random forest, and multi-layer perceptron) were compared to id

SUBMITTER: Zhou S 

PROVIDER: S-EPMC6284273 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature