Enhancing Patient Safety Event Reporting by K-nearest Neighbor Classifier.
Data quality was placed as a major reason for the low utility of patient safety event reporting systems. A pressing need in improving data quality has advanced recent research focus in data entry associated with human factors. The debate on structured data entry or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technical gap on text mining. The present study suggested a text classification method, k-nearest neighbor (KNN), for predicting subject categories as in our proposed reporting system. Our results demonstrated the feasibility of KNN classifier used for text classification and indicated the advantage of such an application to raise data quality and clinical decision support in reporting patient safety events.
The University of Texas Health Science Center at Houston.
Chen
C
Liang
The University of Texas Health Science Center at Houston.
Yang
Y
Gong
D000068598:Data Accuracy; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D000069550:Machine Learning; D062527:Meaningful Use; D019300:Medical Errors / Q000517:prevention & control; D061214:Patient Safety; D010363:Pattern Recognition, Automated / Q000379:methods; D058996:Quality Improvement; D012308:Risk Management / Q000379:methods*; D014481:United States