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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedFritz BA, Cui Z, Zhang M
Deep-learning model for predicting 30-day postoperative mortality.
The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. In this study the investigators sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. They concluded that a deep-learning time-series model improved prediction compared with models with simple summaries of intraoperative data.
AHRQ-funded; HS024581.
Citation: Fritz BA, Cui Z, Zhang M .
Deep-learning model for predicting 30-day postoperative mortality.
Br J Anaesth 2019 Nov;123(5):688-95. doi: 10.1016/j.bja.2019.07.025..
Keywords: Adverse Events, Health Information Technology (HIT), Mortality, Risk, Surgery