Modular Data System for Multimodal Health Condition Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep reinforcement learning (DRL) techniques, particularly Dueling Q-Networks (DQNs) and convolutional neural networks (CNNs), face challenges in handling variations in data presentation and require significant compute resources, leading to difficulties in diagnosing and managing long-term health conditions with multiple and varying symptoms, such as menopause and autoimmune disorders, where current methods result in inefficient treatment regimes and potential adverse outcomes.
Innovation Solution
The use of stacked autoencoders, including variational autoencoders, in conjunction with DQNs, along with a modular data processing system integrating deep reinforcement learning, dueling network architecture, and case-based reasoning, to provide a harmonized data analysis pipeline that processes multimodal and multilevel data, enabling personalized intervention plans and continuous monitoring of patient health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Dueling Q-Networks (DQNs) with convolutional neural networks (CNNs) are used to process health condition data, then the system can handle complex multimodal data and provide personalized recommendations, but the compute resources required increase significantly and training complexity increases
Solution Approach 1:
The patent segments the monolithic DQN architecture into separate value network and advantage network components that can be trained independently. This segmentation allows for more efficient resource utilization during training, as each network can be optimized separately rather than requiring full DQN training resources for all tasks.
Solution Approach 2:
The patent implements a unified data processing pipeline that handles multiple data modalities (electronic health records, genomic data, lifestyle data, etc.) through a single modular architecture. This universal pipeline reduces redundant compute resources by processing diverse data types through shared preprocessing and feature extraction layers.
2Reliability
If DQNs are used to provide personalized treatment recommendations, then treatment effectiveness can be improved, but the training time and computational cost increase
Solution Approach 1:
The patent performs preliminary data processing, feature extraction, and patient stratification before DQN training begins. By pre-processing the health data and organizing it into structured formats, the system reduces the training time required for DQNs while maintaining the ability to provide effective personalized recommendations.
Solution Approach 2:
The patent implements a phased training approach where the system first trains on aggregated population-level data to establish baseline recommendations, then progressively refines with individual patient data. This partial action approach provides timely recommendations sooner rather than waiting for complete individualized training.
3Measurement precision
If data augmentation techniques are used to improve CNN performance on varying data presentations, then classification accuracy improves, but classification mistakes may increase and data integrity is compromised
Solution Approach 1:
The patent changes the approach from data augmentation to parameter optimization by adjusting CNN hyperparameters, learning rates, and architecture configurations to handle data variation. This maintains classification accuracy without introducing potentially harmful data transformations that could cause classification mistakes.
4Reliability
If the system processes all available multimodal data to provide comprehensive recommendations, then recommendation quality improves, but processing time and system complexity increase
Solution Approach 1:
The patent applies local quality by processing different data modalities with specialized processing pipelines tailored to each data type's characteristics. Rather than applying a uniform complex processing approach to all data, each modality (genomic, EHR, lifestyle) receives optimized processing appropriate to its nature, reducing overall system complexity while maintaining comprehensive analysis.
Solution Approach 2:
The system segments the data processing into independent modular pipelines for different modalities that can be executed selectively. This allows the system to process only the necessary data subsets for each patient case rather than always processing all available data, reducing complexity while maintaining recommendation quality.
Data Source
AI summary
The present invention(s) provide systems and methods for identifying one or more long-term health conditions that a patient may be suffering from, and providing an appropriate intervention plan for managing the health condition. In addition, the present invention(s) provide systems and methods for prioritizing among various potential long-term health conditions that a patient may suffer from, and provide an appropriately prioritized intervention plan for managing a variety of health conditions. Finally, the present invention(s) provide systems and methods for continuously monitoring patient health and/or outcome data to appropriately change an intervention plan based on the specific response that may be exhibited by a patient. In this manner the present invention(s) provide a systematized approach for managing long-term health conditions that previously required guesswork and continuous trial and error.