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Updated: May 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Considerations for screening tool selection and role of predictive and concurrent validity.

Marinos Elia¹, Rebecca J Stratton

¹Institute of Human Nutrition, University of Southampton and University of Southampton NHS Hospitals Trust, Southampton, UK. elia@soton.ac.uk

Current Opinion in Clinical Nutrition and Metabolic Care

|August 12, 2011

Summary

Selecting nutrition screening tools is complex. This review highlights that over-reliance on concurrent and predictive validity can be misleading, emphasizing a framework for appropriate tool selection in nutrition programs.

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Last Updated: May 30, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Nutrition Science
Clinical Assessment
Health Outcomes Research

Background:

Nutrition screening tool selection presents challenges for healthcare professionals.
Validity, including concurrent and predictive measures, is a key consideration in tool selection.
Recent studies have focused on these validity metrics, yielding varied results.

Purpose of the Study:

To critically examine the relevance of concurrent and predictive validity in nutrition screening tool selection.
To provide an operational framework to guide the appropriate selection of nutrition screening tools.
To contextualize findings on tool validity within a practical selection process.

Main Methods:

Systematic review of studies evaluating concurrent and predictive validity of nutrition screening tools.
Analysis of the methodologies and results of validity studies.
Development of a conceptual framework for screening tool selection.

Main Results:

Studies on predictive and concurrent validity show inconsistent results due to tool heterogeneity and lack of a gold standard.
Using one tool as a gold standard for another can be misleading.
Observed outcomes without nutritional intervention may not accurately reflect a tool's utility for assessing nutritional needs.

Conclusions:

Over-reliance on concurrent and predictive validity can be counterproductive if applied without considering the tool's purpose.
A comprehensive framework considering multiple factors is essential for effective nutrition screening tool selection.
Ensuring harmony between the screening tool and the screening program is crucial for successful implementation.