Semi-supervised classification of patient safety event reports

The Veterans Health Administration patient safety reporting system receives more than 100,000 reports annually. The information contained in these reports is primarily in the form of natural language text. Improving the ability to efficiently mine these patient safety reports for information is the objective of a proposed semi-supervised method.

A semi-supervised classification method leverages information from both labeled and unlabeled reports to predict categories for the unlabeled reports.

Two different scenarios involving a semi-supervised learning process are examined, and both demonstrate good predictive results.

The semi-supervised method shows much promise in assisting researchers and analysts toward accurately and more quickly separating reports of varying and often overlapping topics. The method is able to use the "stories" provided in patient safety reports to extend existing patient safety taxonomies beyond their static design.