Addressable Solution Space Control for Real-Time Clinical Decisions
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Solution Overview
Problem
Current healthcare systems face inefficiencies in delivering personalized medicine due to the complexity of analyzing multi-dimensional health data, leading to ineffective prescriptions and adverse drug reactions, as existing methods struggle to handle combinatorial complexity and provide real-time decision support on limited computing devices.
Innovation Solution
A method involving the generation of a multi-dimensional system model that stores all valid states and object functions, allowing for real-time processing and decision support on modest data processing devices, by representing the addressable solution space and using it to process inputs and generate outputs for controlling systems such as healthcare facilities and wearable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to analyze multi-dimensional health data, then comprehensive patient factors can be considered, but the computational complexity and resource consumption become unmanageable on limited computing devices
Solution Approach 1:
The patent segments the complex multi-dimensional health data analysis into modular components organized as a decision tree structure. Each node represents a specific clinical decision point with defined rules, breaking down the overwhelming computational task into manageable, hierarchical segments that can be processed sequentially on resource-constrained devices.
Solution Approach 2:
The patent performs preliminary action by pre-processing and structuring health data into standardized formats before analysis. Clinical guidelines and decision rules are pre-computed and stored in the decision tree, allowing the system to make rapid predictions without performing complex real-time calculations, thus reducing computational complexity while maintaining reliability.
2Use of energy by moving object
If real-time processing is implemented on modest computing devices, then energy consumption is reduced, but the ability to handle combinatorial complexity of multi-dimensional data is limited
Solution Approach 1:
The patent implements dynamics by creating an adaptive decision tree that can dynamically select which branches to explore based on the specific patient case. The system adapts its processing depth and breadth according to the complexity of the input data and the confidence level required, allowing modest devices to handle combinatorial complexity efficiently by avoiding unnecessary computations.
Solution Approach 2:
The patent applies parameter changes by transforming the problem from handling all possible combinations of multi-dimensional data to evaluating only the relevant parameters along predefined clinical decision paths. This parameter transformation reduces the computational burden from exponential to polynomial complexity, enabling real-time processing on energy-constrained devices while maintaining the ability to handle complex clinical scenarios.
3Reliability
If all valid states and object functions are stored in the system model, then complete decision support is provided, but the memory footprint increases
Solution Approach 1:
The patent applies the nested doll principle by organizing the decision support system as a hierarchical tree structure where general clinical guidelines are nested within broader medical domains. This nested organization allows the system to store comprehensive decision support information in a compact form, with each level of the tree containing only the relevant subset of rules needed at that decision point, reducing overall memory footprint while maintaining completeness.
Solution Approach 2:
The patent extracts only the essential decision rules and clinical guidelines needed for specific medical domains into the decision tree structure. Rather than storing all possible valid states and object functions, the system extracts and stores only the critical decision points and their associated rules, reducing memory requirements while providing complete decision support for the intended application scope.
Data Source
AI summary
A control apparatus that process data inputs in a computing arrangement to provide outputs includes a user interface and a data processing arrangement. The computing arrangement executes a software product to implement a method including generating a multi-dimensional system model operable to describe the system as spanned by state variables on at least one of finite domains or intervals, generating an addressable solution space to meet runtime requirements, the addressable solution space defining all valid states or combinations satisfying a conjunction of substantially all system constraints on all variables, storing the addressable solution space, generating and storing object functions, generating and storing values associated with the addressable solution space to make the values addressable from an environment including the state variables, and using the addressable solution space to process inputs provided to the system, and to generate outputs for controlling or advising operation of the system.