Towards personalized nutritional treatment for malnutrition using machine learning-based screening tools

Orit Raphaeli, Pierre Singer

Research output: Contribution to journalEditorial

10 Scopus citations

Abstract

Early identification of patients at risk of malnutrition or who are malnourished is crucial in order to start a timely and adequate nutritional therapy. Yet, despite the presence of many nutrition screening tools for use in the hospital setting, there is no consensus regarding the best tool as well as inadequate adherence to screening practices which impairs the achievement of effective nutritional therapy. In recent years, artificial intelligence and machine learning methods have been widely used, across multiple medical domains, to aid clinical decision making and to improve quality and efficiency of care. Therefore, Yin and colleagues propose a machine learning based individualized decision support system aimed to identify and grade malnutrition in cancer patients by applying unsupervised and supervised machine learning methods on nationwide cohort. This approach, demonstrate the ability of machine learning methods to create tools to recognize malnutrition. The machine learning based screening serves as a first layer in a nutritional therapy workflow and provides improved support for decision making of health professionals to fit individualized nutritional therapy in at-risk patients.

Original languageEnglish
Pages (from-to)5249-5251
Number of pages3
JournalClinical Nutrition
Volume40
Issue number10
DOIs
StatePublished - Oct 2021

Keywords

  • Artificial intelligence
  • Clinical nutrition decision support
  • Machine learning
  • Malnutrition
  • Nutritional risk screening

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