MCI Progression Prediction Using Four Clinical Variables
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Solution Overview
Problem
Accurately predicting the progression of mild cognitive impairment (MCI) or mild Alzheimer's dementia (mild AD) is challenging due to lighter symptoms, and existing methods often impose a heavy processing load on patients and equipment.
Innovation Solution
An information processing device and method that utilizes a prediction model trained with variables such as orientation, memory, biomarkers, and IADL activities to predict the probability of symptom advancement in MCI or mild AD, using input variables like orientation to time, delayed recall, memory of holidays and medication schedules, and amyloid β status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex prediction models with many variables are used to accurately predict MCI or mild AD progression, then prediction accuracy is improved, but processing load on patients and equipment increases
Solution Approach 1:
The patent extracts and identifies the four most critical variables from among many potential predictors: orientation to time, delayed recall, memory of IADL events, and amyloid β status. By selecting only these essential variables, the model achieves accurate prediction while minimizing processing load on patients and equipment.
Solution Approach 2:
The patent changes the parameters by focusing on specific, measurable biomarkers and cognitive functions rather than comprehensive assessments. The use of amyloid β status as a binary positive/negative variable and standardized cognitive test scores transforms complex clinical data into manageable parameters that maintain prediction accuracy while reducing complexity.
2Reliability
If comprehensive cognitive assessments are performed to predict disease progression, then prediction reliability is improved, but ease of operation deteriorates due to increased burden on patients
Solution Approach 1:
The patent extracts only the four most reliable predictors from comprehensive cognitive assessments, eliminating unnecessary test components. This extraction maintains prediction reliability by focusing on the most discriminative variables while significantly reducing the time and effort required from patients.
Solution Approach 2:
The patent applies partial action by performing only the essential cognitive assessments needed for reliable prediction rather than comprehensive evaluations. The four selected variables provide sufficient predictive power without requiring exhaustive testing, thereby improving ease of operation while maintaining reliability.
Data Source
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AI summary
An information processing device inputs information of a patient to a prediction model trained using training data, in which at least four variables that fall under at least any of first information relating to orientation, second information relating to memory, third information relating to memory of a holiday, a family gathering, a reservation, or schedule of taking medication in IADL, or memory of a meal or actual work of travel in IADL, and fourth information relating to positive/negative of a biomarker are associated with presence/absence of advance of a symptom of mild cognitive impairment (MCI) or mild Alzheimer's dementia (mild AD), with the information of the patient corresponding to each of the at least four variables, and predicts a probability of advance of a symptom of mild cognitive impairment (MCI) or mild Alzheimer's dementia (mild AD) in the patient. The at least four variables include a first variable that is information relating to orientation relating to time out of the first information, a second variable that is information relating to delayed recall out of the second information, a third variable that is information relating to memory regarding a holiday, a family gathering, a reservation, or schedule for taking medication in IADL, out of the third information, and a fourth variable that is information relating to amyloid β positive/negative out of the fourth information.