Semi-Supervised Classification of Patient Safety Event Reports

OBJECTIVESThe 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 informati...

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Bibliographic Details
Published in:Journal of patient safety 2012-06, Vol.8 (2), p.60-64
Main Author: McKnight, Scott D.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Documentation - methods
Humans
Information Storage and Retrieval
Mandatory Reporting
Original Article
Patient Safety - statistics & numerical data
Safety Management - classification
Safety Management - methods
United States
United States Department of Veterans Affairs
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Summary:OBJECTIVESThe 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. METHODSA semi-supervised classification method leverages information from both labeled and unlabeled reports to predict categories for the unlabeled reports. RESULTSTwo different scenarios involving a semi-supervised learning process are examined, and both demonstrate good predictive results. CONCLUSIONSThe 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.
ISSN:1549-8417
1549-8425
DOI:10.1097/PTS.0b013e31824ab987