Automated Ascertainment of Typhlitis From the Electronic Health Record.

Tamara P Miller Yimei Li Aaron J MasinoEmma ValleeEvanette BurrowsMark RamosTodd A AlonzoRobert GerbingSharon M CastellinoDouglas S Hawkins Timothy L Lash Richard Aplenc Robert W Grundmeier

Published in: JCO clinical cancer informatics (2022)

The automated algorithm identified true cases of typhlitis with higher sensitivity than COG reporting. The algorithm identified false positives but reduced the number of courses needing manual review by 96% (961 to 37) by detecting potential typhlitis. This algorithm could provide a useful screening tool to reduce manual effort required for typhlitis AE reporting.

Keyphrases

deep learning
machine learning
electronic health record
adverse drug
high throughput
clinical decision support
emergency department