Development and validation of a deep learning model for detection of allergic reactions using safety event reports across hospitals.

Jie Yang; Liqin Wang; Neelam A Phadke; Paige G. Wickner; Christian M Mancini; Kimberly Blumenthal; Li Zhou

Study

Development and validation of a deep learning model for detection of allergic reactions using safety event reports across hospitals.

December 9, 2020

Yang J, Wang L, Phadke NA, et al. JAMA Netw Open. 2020;3(11):e2022836.

View more articles from the same authors.

Artificial intelligence can support improved patient safety outcomes. This study found that a deep learning model can accurately and efficiently identify allergic reactions in hospital safety reports and can potentially enable real-time event surveillance and system improvement.

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Development and validation of a deep learning model for detection of allergic reactions using safety event reports across hospitals.

Remote Response Team and Customized Alert Settings Help Improve Management of Sepsis

Reducing hospital harm: establishing a command centre to foster situational awareness.

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