Gastrointestinal failure, big data and intensive care

Enteral feeding is the main route of administration of medical nutritional therapy in the critically ill. However, its failure is associated with increased complications. Machine learning and artificial intelligence have been used in intensive care to predict complications. The aim of this review is to explore the ability of machine learning to support decision making to ensure successful nutritional therapy.

Numerous conditions such as sepsis, acute kidney injury or indication for mechanical ventilation can be predicted using machine learning. Recently, machine learning has been applied to explore how gastrointestinal symptoms in addition to demographic parameters and severity scores, can accurately predict outcomes and successful administration of medical nutritional therapy.

With the rise of precision and personalized medicine for support of medical decisions, machine learning is gaining popularity in the field of intensive care, first not only to predict acute renal failure or indication for intubation but also to define the best parameters for recognizing gastrointestinal intolerance and to recognize patients intolerant to enteral feeding. Large data availability and improvement in data science will make machine learning an important tool to improve medical nutritional therapy.