Cognitive Screening and Treatment Platform with Adaptive Recommendations
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Solution Overview
Problem
There is a growing concern and unmet need for personalized cognitive treatment and monitoring of immune-mediated and neuro-degenerative disorders, as existing treatments often fail to account for the heterogeneous nature of cognitive impairments in individuals with these conditions, leading to inadequate remediation therapies.
Innovation Solution
A system and method for generating personalized cognitive treatment recommendations using a predictive model trained on data from individuals with immune-mediated and neuro-degenerative disorders, incorporating physiological and clinical data to tailor treatment regimens based on individual cognitive profiles, including tools for interference processing, spatial navigation, and emotional processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standardized cognitive treatments are used for all patients with immune-mediated or neuro-degenerative disorders, then treatment delivery is simplified and resource utilization is improved, but treatment effectiveness deteriorates due to heterogeneous cognitive impairments across individuals
Solution Approach 1:
The patent segments cognitive treatments into multiple distinct tools (e.g., interference processing tool, spatial navigation tool, emotional processing tool) that can be selectively applied. Each tool targets specific cognitive domains affected by immune-mediated or neuro-degenerative disorders, allowing customization based on individual patient profiles while maintaining structured delivery through a computing system.
Solution Approach 2:
The system dynamically adapts treatment recommendations based on patient responses, performance data, and changing cognitive profiles. The computing system generates updated treatment recommendations over time, adjusting the type, frequency, and intensity of cognitive tools based on measured outcomes, thereby optimizing effectiveness while maintaining systematic delivery.
2Reliability
If personalized cognitive treatment recommendations are generated using multiple data sources and predictive models, then treatment effectiveness is improved, but system complexity increases
Solution Approach 1:
The computing system performs multiple functions within a single integrated platform: collecting physiological and clinical data, processing performance metrics from cognitive tasks, generating treatment recommendations, and monitoring outcomes. This multi-functional approach consolidates complexity into a unified system rather than requiring separate systems for each function.
Solution Approach 2:
The system automatically generates treatment recommendations by processing patient data through predictive models without requiring manual intervention for each decision. The automated generation of personalized treatment plans based on algorithmic analysis of multiple data sources reduces the burden on clinicians while maintaining high customization levels.
3Ease of operation
If cognitive treatment tools are made accessible outside clinical settings, then patient accessibility is improved, but monitoring and personalization capabilities deteriorate
Solution Approach 1:
The system continuously collects performance data from patients as they complete cognitive tasks and uses this feedback to generate updated treatment recommendations. This closed-loop feedback mechanism ensures that even when patients access treatments outside clinical settings, the system maintains accurate monitoring capabilities by automatically processing patient responses and adjusting recommendations accordingly.
Solution Approach 2:
The computing system acts as an intermediary between patients and clinicians, enabling patients to access cognitive treatment tools remotely while maintaining connection to the monitoring and personalization infrastructure. The system mediates data collection, analysis, and recommendation generation, allowing decentralized delivery without sacrificing monitoring precision.
Data Source
AI summary
Systems and methods for generating a personalized cognitive treatment recommendation for an individual. The system includes one or more processors; and a memory to store processor-executable instructions. Upon execution of the instructions, the one or more processors receive parameters for at least one cognitive treatment tool; receive physiological data indicative of a condition of the individual, and/or clinical data associated with the individual; and generate the personalized cognitive treatment recommendation based on the physiological data and/or the clinical data. The recommendation includes a specification of (i) at least one first cognitive treatment tool, (ii) at least one second cognitive treatment tool different from the at least one first cognitive treatment tool, or (iii) both (i) and (ii). Optionally, the one or more processors receive performance data indicative of the individual's performance of at least one task associated with the at least one cognitive treatment tool of the recommendation.