Neurocognitive Screening via Personalized Memory Recall Sessions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for detecting and measuring neurocognitive decline are generic, subjective, and often conducted in clinical settings, leading to late detection and inadequate treatment options by the time individuals seek medical assistance.
Innovation Solution
A computer-implemented method and system using a trained interactive-generating machine learning model to create personalized memory recall sessions based on historical user data, incorporating facial, handwriting, speech, and psychosocial analysis to determine neurocognitive impairment, allowing for continuous monitoring and early detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional clinical methods are used for detecting neurocognitive decline, then the detection process is simple and accessible, but the detection is late and the measurements are generic and subjective
Solution Approach 1:
The assessment is divided into multiple independent analysis modules: facial analysis, handwriting analysis, speech analysis, and psychosocial/economic health analysis. Each module processes specific data types separately before integrating results, allowing high-precision measurement while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system integrates multiple data collection and analysis functions into a single comprehensive platform. The same machine learning model framework handles diverse data types (facial, handwriting, speech) and multiple assessment objectives, achieving precise neurocognitive detection without proportionally increasing system complexity
2Measurement precision
If generic subjective questions are used in clinical settings, then the assessment is easy to administer, but it fails to capture individual-specific neurocognitive states
Solution Approach 1:
The system tailors assessment content and parameters to each individual user based on their unique characteristics and historical data. The machine learning model generates personalized interactives and analysis parameters specific to each user, enabling precise individualization while the automated nature maintains ease of operation
Solution Approach 2:
The system performs preliminary data collection and analysis before the actual assessment. Historical user data is gathered and processed in advance, and personalized interactives are generated beforehand, so that when the assessment occurs, it can immediately provide precise individualized results without adding complexity to the user experience
3Loss of time
If clinical assessments are conducted when individuals seek medical assistance, then the evaluation is thorough, but neurocognitive decline has already progressed significantly
Solution Approach 1:
The system enables continuous monitoring and assessment of neurocognitive function through repeated interactions over time. Rather than a single clinical visit, users engage with the system periodically, allowing early detection of decline trends before they become severe, thereby reducing time loss and improving treatment effectiveness
Solution Approach 2:
The system provides continuous feedback to users about their neurocognitive status based on analysis of their responses and behavioral patterns. This ongoing feedback mechanism enables early identification of deterioration trends, prompting timely intervention before significant progress is made, thus reducing the time lag between onset and detection
Data Source
AI summary
Systems and methods include receiving at least one data entry associated with a user from a user device, determining historical user data based on the at least one data entry, receiving a request to initiate a memory recall session from the user device, determining interactive(s) specific to the user based on at least a portion of the historical user data, transmitting the interactive(s), causing the user device to display the interactive(s) during the memory recall session, receiving response(s) of the user to the interactive(s), determining an interactive result specific to the user based on the response(s), determining supplemental data associated with the response(s), determining a neurocognitive result based on the interactive result and the supplemental data, and transmitting the neurocognitive result for display via a graphical user interface of the user device.