Health Science Decision Support System for Precision Diagnosis
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Solution Overview
Problem
Current health sciences face challenges in rapidly diagnosing new or mutated diseases, discovering effective drugs, understanding genetic diseases, and optimizing treatments due to limitations in processing and analyzing vast amounts of life-science data effectively.
Innovation Solution
A Health Science Decision Support System (HSDSS) employing advanced analytics, including nonlinear manifold clustering and neural networks, to process and analyze health-related data, providing optimized recommendations for diagnosis and treatment by identifying key data patterns and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional data processing methods are used to analyze life-science data, then the system is simpler and easier to implement, but the processing speed and diagnostic accuracy are insufficient for rapidly diagnosing new or mutated diseases
Solution Approach 1:
The patent segments the analysis of life-science data into multiple dimensions including genetic data, clinical data, environmental data, and lifestyle data. Each dimension is processed separately through dedicated analytics modules before being integrated to form comprehensive diagnostic recommendations, enabling faster parallel processing of complex datasets
Solution Approach 2:
The patent transforms traditional linear data processing into multi-dimensional analysis by incorporating genetic, epigenetic, proteomic, metabolomic, and microbiomic data layers. This dimensional expansion allows the system to process and correlate diverse data types simultaneously, dramatically increasing processing speed and diagnostic capability
2Productivity
If manual analysis of Electronic Medical Records is performed, then data processing is simpler, but the burden of analysis is high and time-consuming
Solution Approach 1:
The system implements automated self-service capabilities where the analytics engine automatically ingests, cleans, validates, and processes Electronic Medical Records without manual intervention. The system self-optimizes by continuously learning from new data and automatically updating diagnostic algorithms, eliminating time-consuming manual analysis while maintaining high productivity
Solution Approach 2:
The patent replaces manual mechanical analysis of medical records with automated computational systems including natural language processing, machine learning algorithms, and artificial intelligence. This substitution eliminates the time burden of manual review while dramatically increasing analysis productivity through parallel processing of vast medical data
3Measurement precision
If comprehensive life-science data is collected for precision medicine, then diagnostic accuracy improves, but the complexity of data management and analysis increases
Solution Approach 1:
The patent creates a universal data management platform that handles multiple types of life-science data (genomic, epigenomic, proteomic, metabolomic, microbiomic, clinical, environmental) through a single integrated system. This multi-functional platform maintains diagnostic accuracy while reducing management complexity by providing unified data ingestion, storage, processing, and visualization capabilities across all data types
Solution Approach 2:
The system introduces intermediate data layers including data normalization standards, integration protocols, and intermediary processing modules that bridge diverse data sources. These intermediaries translate and harmonize data from different formats and sources into a unified structure, maintaining diagnostic precision while simplifying the complexity of managing comprehensive life-science data
4Difficulty of detecting and measuring
If traditional clustering methods are used for data analysis, then the processing is faster, but the ability to detect obscure structures and patterns in data is limited
Solution Approach 1:
The patent implements dynamic, adaptive clustering algorithms that automatically adjust their parameters and complexity based on the characteristics of the input data. The system transitions between different clustering methodologies (hierarchical, k-means, density-based, spectral) depending on data density, dimensionality, and structure, enabling detection of obscure patterns while managing algorithmic complexity through adaptive selection
Data Source
AI summary
A method and apparatus can include a system controller and a system processor. The system controller can retrieve a health science related dataset from at least one database, the retrieved dataset including information associated with at least one of a patient medical information, healthcare provider clinical information, health related publications and treatment information, and pharmaceutical information, and transmit to a user equipment a recommendation of at least one of health diagnosis and treatment for a patient. The system processor can utilize multidimensional nonlinear manifold clustering on at least one element from the retrieved dataset, assign an entity formulated from the at least one element of the retrieved dataset into a decision hyper-volume based on the multidimensional nonlinear manifold clustering, and determine the recommendation of at least one of health diagnosis and treatment for the patient based on the assignment of the entity into the decision hyper-volume.