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HEROI²C

@Heroi2C

Hybrid machinE leaRning for Improved Infection management in Critically ill An @IDLabResearch @uzgent project Follow for updates on #AI and #sepsis management

 Ghent, Belgium
heroi2c.ugent.be
Joined July 2020
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Photo by HEROI²C (@Heroi2C)Photo by HEROI²C (@Heroi2C)Photo by HEROI²C (@Heroi2C)Photo by HEROI²C (@Heroi2C)Photo by HEROI²C (@Heroi2C)Photo by HEROI²C (@Heroi2C)
We now have a website! You can find us at heroi2c.ugent.be/. It is a work-in-progress; any feedback is appreciated! Make sure to also follow our Researchgate for the latest updates: researchgate.net/project/HER…
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Is this another step toward heart disease prevention and AI in healthcare? Interested? The pre-proof of our article where we attempt to provide a risk estimation prediction for Atrial Fibrillation in the ICU is available! Go have a read! sciencedirect.com/science/ar…
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Our group just published a brand new proof of concept to optimize antimicrobial dosing in the intensive care unit (ICU) in @BioMedCentral Medical Informatics and Decision Making. 👇 bmcmedinformdecismak.biomedc… Want to know what we did? A 🧵 (1/14).
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The final aim is deployment in clinical practice in the intensive care unit by integration within the electronic health record to predict plasma concentrations and give dose guidance in real-time. (13/14)
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Still a long way to go, but @AiVerhaeghe, @ThomasDCorte, @fongenae, @svhoecke and @criticcaredoc are up for the challenge! With special thanks to @sofie_dhaese and @FWOVlaanderen! (14/14)
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Our best performing model was the Gradient Boosting Trees (GBT) model. Performance metrics were comparable between the publisehd PopPK model and the GBT model. (7/)
Image from HEROI²C (@Heroi2C)
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As predicted drug concentrations are point estimates, reporting the degree of uncertainty is important for clinical decision-making. Using the Quantile Ensemble method, the GBT models is able to produce reliable prediction intervals and predictive distributions. (8/)
Image from HEROI²C (@Heroi2C)
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