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

Orit Raphaeli1, Pierre Singer2

  • 1Industrial Engineering and Management, Ariel University, Israel; General Intensive Care Department and Institute for Nutrition Research, Rabin Medical Center, Beilinson Hospital. Affiliated to Sackler School of Medicine, Tel Aviv University, Israel.

Clinical Nutrition (Edinburgh, Scotland)
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Early malnutrition identification is vital for effective nutritional therapy. Machine learning offers a promising approach to accurately screen cancer patients, improving clinical decision-making and personalized care.

Keywords:
Artificial intelligenceClinical nutrition decision supportMachine learningMalnutritionNutritional risk screening

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Area of Science:

  • Oncology
  • Medical Informatics
  • Nutritional Science

Background:

  • Effective nutritional therapy relies on early identification of malnutrition in patients.
  • Current nutrition screening tools lack consensus and adherence, hindering effective therapy.
  • Artificial intelligence (AI) and machine learning (ML) show potential in medical decision support.

Discussion:

  • This study introduces an ML-based system for individualized malnutrition identification and grading in cancer patients.
  • The system utilizes unsupervised and supervised ML methods applied to a nationwide cohort.
  • This approach demonstrates ML's capability in developing robust malnutrition screening tools.

Key Insights:

  • Machine learning can effectively identify and grade malnutrition in cancer patients.
  • An ML-based screening system can serve as a foundational layer in nutritional therapy workflows.
  • This technology enhances clinical decision support for personalized nutritional interventions.

Outlook:

  • Further validation of ML-based screening tools is essential for widespread clinical adoption.
  • Integration of such systems could standardize and improve nutritional care in oncology.
  • Future research may explore AI's role in predicting malnutrition risk and optimizing treatment pathways.