Cognitive Progression Prediction Using IADL and Biomarker Features
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Solution Overview
Problem
Existing technologies struggle to accurately predict the progression of mild cognitive impairment (MCI) or mild Alzheimer's dementia (mild AD) due to the subtlety of symptoms in these conditions.
Innovation Solution
An information processing device and method that utilizes a prediction model trained on patient information including variables such as orientation, remembering, IADL activities, and biomarker positivity/negativity to predict the probability of symptom progression in MCI or mild AD, using integrated and divided feature amounts to correlate symptom magnitudes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction methods are used for MCI or mild AD patients, then the prediction process is simple, but the prediction accuracy is low due to mild symptoms
Solution Approach 1:
The prediction model segments patient information into multiple distinct variables: orientation information, remembering information, IADL information (remembering holidays/family gatherings/appointments/medication schedules and actual actions), and biomarker information. This segmentation allows the model to capture subtle aspects of mild cognitive impairment separately and comprehensively, improving prediction accuracy for MCI and mild AD cases where symptoms are not yet severe.
Solution Approach 2:
The patent introduces a multi-dimensional assessment framework by evaluating patients across four distinct information dimensions (orientation, remembering, IADL, biomarkers) rather than relying on single-dimensional conventional methods. This dimensional expansion enables detection of subtle cognitive changes in MCI and mild AD that would be missed by traditional single-metric assessments.
2Measurement precision
If multiple variables are used to improve prediction accuracy, then the prediction becomes more comprehensive, but the data processing complexity increases
Solution Approach 1:
The prediction model is designed to universally process multiple types of patient information (orientation, remembering, IADL, biomarkers) through a unified machine learning framework. This multi-functional approach allows the same model structure to handle diverse data types, making the complex data collection process manageable and systematic while maintaining high prediction accuracy for MCI and mild AD progression.
Data Source
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AI summary
Patient information corresponding to each of at least three variables is input to a prediction model trained using training data in which the three variables are associated with presence or absence of progression of symptoms of mild cognitive impairment (MCI) or mild Alzheimer's dementia (mild AD) to predict a probability of progression of patient's symptoms of mild cognitive impairment (MCI) or mild Alzheimer's dementia (mild AD), the at least three variables being based on information selected from among first information on orientation, second information on remembering, third information on remembering a holiday, a family gathering, an appointment, or a medication schedule in IADL or an actual action of eating, shopping, or traveling in IADL, and fourth information on positivity and negativity biomarkers, wherein, with respect to at least four pieces of information, selected from the first information, the second information, the third information, and the fourth information and including: at least one of information on orientation relating to time from among the first information and information on remembering a holiday, a family gathering, an appointment, or a medication schedule in IADL from among the third information; information on delayed recall from among the second information; and information on positivity and negativity of amyloid β from among the fourth information, the at least three variables include at least one of a first feature amount obtained by integrating information in which evaluation values indicating degrees of symptoms of mild cognitive impairment (MCI) or mild Alzheimer's dementia (mild AD) show a positive correlation, and a second feature amount obtained by dividing information in which magnitudes of evaluation values indicating degrees of symptoms of mild cognitive impairment (MCI) or mild Alzheimer's dementia (mild AD) show a negative correlation.