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• W3000337247 abstract "The early prediction of sepsis in intensive care units using clinical data is the objective of the PhysioNet/Computing in Cardiology Challenge 2019. In this paper, a machine learning approach is presented which uses an optimized Random Forest for prediction of a septic condition. After an initial data augmentation step, a customized learning process is performed for the trees to consider imbalance in the dataset. Finally, a feature reduction is implemented and the forest is trimmed to 50 trees for an optimal classification in terms of run time and accuracy. Using a 10-fold cross-validation on the complete training dataset, a mean utility score of 0.376 is achieved. In the final submission, a normalized observed utility score of 0.296 on the full test set is achieved. Our team name is The Septic Think Tank (final rank: 21)." @default.
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• W3000337247 date "2019-12-30" @default.
• W3000337247 modified "2023-09-24" @default.
• W3000337247 title "Early Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data" @default.
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• W3000337247 doi "https://doi.org/10.23919/cinc49843.2019.9005769" @default.
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