Normalized Learning Health System for Multi-Source Medical Data Integration
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Solution Overview
Problem
Current healthcare systems lack an efficient method to normalize and integrate multiple organ and disease-specific laboratory tests, vital signs, genetic factors, and patient history for comprehensive disease risk assessment and monitoring, leading to suboptimal disease diagnosis and management.
Innovation Solution
A method and system for an interoperable normalized learning health system (NLHS) that combines and weights multiple medical test results, vital signs, and genetic data to provide a unified disease test parameter, using a graphical user interface to present normalized data and alert physicians or patients to abnormal results, leveraging AI and machine learning for refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple organ and disease-specific laboratory tests, vital signs, genetic factors, and patient history are integrated for comprehensive disease risk assessment, then diagnostic accuracy and disease monitoring capability are improved, but system complexity and data processing difficulty increase
Solution Approach 1:
The system segments diverse medical data into distinct categories (laboratory tests, vital signs, genetic factors, patient history) and processes each category through dedicated normalization pathways before integration, reducing the complexity of handling heterogeneous data while maintaining comprehensive diagnostic capability
Solution Approach 2:
The system transforms multiple different types of medical parameters into a unified normalized scale using mathematical transformations and weighting factors, enabling comprehensive disease risk assessment while simplifying the integration process through parameter standardization
2Adaptability or versatility
If multiple different types of medical data are normalized to a unified scale, then data integration and comparison capability are improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary normalization and weighting of medical data at the point of data entry and storage, so that when data needs to be integrated for disease risk assessment, the normalization work has already been completed, reducing real-time processing time
Solution Approach 2:
The system implements a universal normalization framework that can handle multiple types of medical data (laboratory tests, vital signs, genetic factors) through a single integrated process, improving adaptability while avoiding the need for separate processing pipelines that would increase time consumption
3Reliability
If comprehensive medical data from multiple sources is integrated for disease risk assessment, then disease monitoring capability is improved, but data heterogeneity and integration difficulty increase
Solution Approach 1:
The system applies parameter transformation to convert heterogeneous medical data from different sources into a unified normalized scale with consistent weighting, enabling reliable disease monitoring while reducing integration difficulty through standardization
Solution Approach 2:
The system introduces a normalization layer as an intermediary between diverse data sources and the disease risk assessment engine, translating various data formats and scales into a common framework, thereby improving reliability while managing integration complexity
Data Source
AI summary
A method, system, apparatus, and computer program product for interoperative normalized learning health system (NLHS) disease test and scale for health and disease risk, disease monitoring, and disease diagnosis based on combined multiple normalized and weighted, organ and selected disease specific laboratory tests, vital signs/measurements, history, genetics, and observations.