Biometric Signal Analysis for Personalized Disease Prediction
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Solution Overview
Problem
Existing approaches for diagnosing and predicting disease comorbidity rely on analyzing individual biomarkers or using single factor statistical models, which fail to account for the complex interplay of multiple biomarkers and individual variations in body morphology. Additionally, these methods lack portability and security in handling personal medical data.
Innovation Solution
A mobile device-enabled diagnostic system that utilizes a data analytics engine to preprocess and analyze biometric signal data, generating personalized biomarker predictions. This system includes a biometric detection device, a trained neural network, and a control circuit configured to receive and process biometric data, predict biomarkers, and provide disease prediction information securely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic systems use complex multi-factor analysis to improve prediction accuracy, then disease prediction accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The system segments the complex diagnostic task into multiple independent components: biometric data collection modules, neural network processing modules, and prediction output modules. Each component handles specific aspects of disease prediction independently, allowing the system to achieve high accuracy through coordinated simple modules rather than a single complex system.
Solution Approach 2:
The patent introduces an intermediary neural network processing layer that mediates between raw biometric data and disease prediction outputs. This intermediary layer processes and transforms the complex relationships between multiple biometric factors, enabling accurate predictions without requiring direct complex analysis in the final output system.
2Adaptability or versatility
If comprehensive biometric data collection is implemented to personalize predictions, then prediction personalization is improved, but data security risks increase
Solution Approach 1:
The system extracts and processes only the necessary biometric features required for disease prediction, separating essential data from unnecessary information. By taking out only the critical biometric parameters (such as body composition metrics, vital signs) needed for personalized predictions, the system minimizes the amount of sensitive data stored and transmitted, thereby reducing security exposure while maintaining prediction personalization.
Solution Approach 2:
The patent uses data copying and encryption mechanisms to create secure representations of biometric data. Instead of storing raw sensitive data directly, the system creates encrypted copies and hashed versions that can be processed for personalization while maintaining security. This allows the system to work with personalized data representations without exposing the original sensitive information.
3Ease of manufacture
If portable devices are used to reduce cost and increase accessibility, then device affordability is improved, but processing power and accuracy are reduced
Solution Approach 1:
The system applies partial action by implementing a streamlined version of the full diagnostic workflow that is optimized for portable devices. Instead of requiring complete complex analysis, the system performs essential biometric data collection and processing steps that are sufficient for accurate predictions on mobile platforms. The neural network is designed to execute partial processing operations that maintain accuracy while fitting within portable device computational constraints.
Data Source
AI summary
A disease prediction system and method detects one or more biometric signal data and is trained to offer predictions, recommendations, and/or diagnosis, disease prediction, treatment or services for one or more patients. The system trains a machine learning algorithm, for example, a neural network and includes: biometric detection device configured to generate biometric signal data of one or more patients; an electronic memory that includes data representing a trained neural network that has been trained to produce biomarker information or information used in diagnosis. Biomarker information is personalized to the individual patient as defined by the biometric signal data to: normalize the biometric signal data with at least one of: smoothing or filtering texture, shading/lighting; create a 3D volumetric mesh and project to generate 2D biometric image; identify a plurality of body morphology feature set data; generate body morphology composition data [BMCD] and biomarkers; predict at least one biomarker.