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Updated: Jul 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Supporting medical planning by mitigating cognitive load
D W Glasspool1, A Oettinger, J H Smith-Spark
1Advanced Computation Laboratory, Cancer Research UK, PO Box 123, London WC2A 3PX, United Kingdom. david.glasspool@cancer.org.uk
Objectives:
Developing a care plan for a patient is a complex task, requiring an understanding of interactions and dependencies between procedures and of their possible outcomes for an individual patient. Decision support for planning has broader requirements than are typically considered in medical informatics applications. We consider the appropriate design of software to assist medical planning.
Methods:
The likely cognitive loads imposed by planning tasks were assessed with a view to directly supporting these via software.
Results:
Five types of cognitive load are likely to be important. A planning support system, REACT, was designed to ameliorate these cognitive loads by providing targeted dynamic feedback during planning. An initial evaluation study in genetic counselling indicates that the approach is successful in that role.
Conclusions:
The approach provides the basis of a general aid for visualizing, customizing and evaluating care plans.
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