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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

Explainable Artificial Intelligence in Ambulatory Digital Dementia Screenings.

Markus Schinle¹, Christina Erler¹, Maximilian Hess¹

¹FZI Research Center for Information Technologies, Germany.

Studies in Health Technology and Informatics

|May 25, 2022

Summary

This summary is machine-generated.

This study evaluates Explainable Artificial Intelligence (XAI) methods for understanding Machine Learning (ML) predictions in a digital dementia screening app. It identifies optimal XAI approaches for reliable early dementia diagnosis.

Keywords:

Alzheimer’s disease dementia screening explainable artificial intelligence interpretable machine learning precision care

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Area of Science:

Digital Health
Artificial Intelligence
Neuroscience

Background:

Digital apps are emerging for early dementia diagnosis via screenings.
Machine Learning (ML) models are increasingly used in these apps to predict cognitive impairment.

Purpose of the Study:

To explain predictions from the DemPredict mobile application using Explainable Artificial Intelligence (XAI).
To compare different XAI methods for suitability in this context.

Main Methods:

Application of various XAI techniques to the DemPredict app's ML predictions.
Evaluation of XAI methods based on trustworthiness, stability, and computation time.
Comparative analysis of XAI approaches for optimal selection.

Main Results:

Identified challenges in comparing results across different XAI methods.
Established criteria for evaluating XAI method performance in this domain.
Determined optimal XAI approaches for specific algorithms used in dementia prediction.

Conclusions:

Explainable Artificial Intelligence (XAI) is crucial for understanding ML-based dementia screening tools.
A systematic evaluation of XAI methods is necessary for trustworthy digital health applications.
This research provides a framework for selecting appropriate XAI techniques for cognitive impairment prediction.