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Gastrointestinal failure, big data and intensive care.

Pierre Singer1,2, Eyal Robinson2, Orit Raphaeli2,3

  • 1Herzlia Medical Center, Intensive Care Unit, Herzlia.

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Summary

Machine learning can predict complications in critically ill patients receiving enteral feeding. This artificial intelligence approach supports decision-making for successful medical nutritional therapy and reduces patient intolerance.

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Area of Science:

  • Critical care medicine
  • Biomedical engineering
  • Data science

Background:

  • Enteral feeding is crucial for critically ill patients but can fail, leading to complications.
  • Machine learning (ML) and artificial intelligence (AI) show promise in predicting complications in intensive care settings.
  • Predicting successful medical nutritional therapy is essential for patient outcomes.

Approach:

  • This review explores ML's capability to aid decision-making for effective enteral feeding.
  • Investigates ML's application in predicting gastrointestinal intolerance and patient outcomes.
  • Analyzes the role of ML in personalized medicine for intensive care.

Key Points:

  • ML models can predict various critical conditions, including sepsis and acute kidney injury.
  • Gastrointestinal symptoms, demographics, and severity scores are used by ML to predict nutritional therapy success.
  • ML helps identify patients intolerant to enteral feeding, enabling personalized interventions.

Conclusions:

  • ML is increasingly vital in intensive care for predicting complications and optimizing patient care.
  • The growing availability of data and advancements in data science will enhance ML's role in medical nutritional therapy.
  • ML supports precision medicine by defining parameters for recognizing and managing enteral feeding intolerance.