Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients

Ya-Xi Wang; Xun-Liang Li; Linghui Zhang; Haina Li; Xiaomin Liu; Wen Song; Xufeng Pang

doi:10.3389/fnut.2023.1060398

April 14, 2023Frontiers in NutritionOpen Access

Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients

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Key Points

To develop and validate an XGBoost machine learning model for early prediction of enteral nutrition initiation in intensive care unit patients.
Trained and evaluated an extreme gradient boosting (XGBoost) machine learning model.
Assessed model performance in predicting the timing of enteral nutrition initiation among critically ill ICU patients.
The established XGBoost model successfully predicted early initiation of enteral nutrition in ICU patients.
The algorithm demonstrated utility as an early automated decision-support tool for clinical nutrition management in critical care.

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Implication

Validation study reveals an XGBoost model accurately predicts early enteral nutrition initiation in ICU patients, highlighting the utility of machine learning in critical care nutrition.

Abstract

The XGBoost model was established and validated for early prediction of EN initiation in ICU patients.

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Cite This Study

Wang et al. (2023) studied this question.

synapsesocial.com/papers/69d77571db9d5e1bf4b8abc9 https://doi.org/10.3389/fnut.2023.1060398

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Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients

Key Points

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Implication

Abstract

Discussion

Cite This Study

Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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