Statistical text classifier to detect specific type of medical incidents.
WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.
Policy Alternatives Research Institute, The University of Tokyo, Japan.
Zoie Shui-Yee
ZS
Wong
Masanori
M
Akiyama
D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration; D019300:Medical Errors / Q000145:classification*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D018875:Vocabulary, Controlled