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Artificial Intelligence-Based Hospital Malnutrition Screening: Validation of a Novel Machine Learning Model.

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An artificial intelligence (AI) model effectively identifies hospital malnutrition risk, outperforming traditional screening tools. This AI approach aids in early detection and better patient outcomes for malnutrition.

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Healthcare
  • Predictive Modeling

Background:

  • In-hospital malnutrition is a significant issue, leading to increased morbidity, mortality, and costs.
  • Current malnutrition screening methods are often underutilized or ineffective.
  • Artificial intelligence (AI) presents a potential solution for improved malnutrition risk identification.

Purpose of the Study:

  • To evaluate an AI-based malnutrition screening model in a large, diverse inpatient population.
  • To compare the AI model's performance against a standard clinician-delivered malnutrition screening tool.

Main Methods:

  • A gradient-boosted decision tree model was developed using electronic medical record data from 106,449 patients.
  • A large language model (LLM) was incorporated for feature extraction.
  • The model's performance was evaluated against discharge-coded and dietitian-recorded malnutrition.

Main Results:

  • The AI model achieved an area under the receiver operating curve of 0.92 on day one, increasing to 0.95 when considering maximum predicted risk.
  • The model significantly outperformed a nurse-administered Malnutrition Screening Tool (MST).
  • Patients identified by the AI model showed higher risks of readmission and mortality compared to those identified by the MST.

Conclusions:

  • The study validates the use of a novel AI model for predicting in-hospital malnutrition.
  • This AI tool demonstrates superior performance in identifying at-risk patients.
  • The findings support the integration of AI into clinical workflows for malnutrition management.