Using multiclass classification to automate the identification of patient safety incident reports by type and severity

Using multiclass classification to automate the identification of patient safety incident reports by type and severity

Abstract Background Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incid...

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Bibliographic Details
Main Authors: Ying Wang, Enrico Coiera, William Runciman, Farah Magrabi
Format: Article
Language:English
Published: BMC 2017-06-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Machine learning
Patient safety
Text mining
Incident reporting
Medical informatics
Online Access:http://link.springer.com/article/10.1186/s12911-017-0483-8

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http://link.springer.com/article/10.1186/s12911-017-0483-8

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