AI Constitutional Analysis Using Objective Functions
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Solution Overview
Problem
Existing methods for analyzing large and varied data sets in the field of objective function optimization face challenges in balancing sophistication and efficiency, particularly in identifying and alleviating disease states using physiological data.
Innovation Solution
A system and method that utilize a computing device to generate a ranked list of diseases based on disease impact score vectors, optimize an objective function, and match user physiological data to disease states, generating curative habitual patterns to alleviate these states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated AI methods are used to analyze large and varied physiological data sets, then measurement precision and reliability improve, but device complexity and computational resources increase
Solution Approach 1:
The system segments the complex analysis task into distinct functional modules: a disease state classifier that processes physiological data to identify disease states, and a curative pattern generator that recommends treatments. This segmentation allows each module to specialize in specific functions, improving measurement precision while managing device complexity through modular architecture.
2Measurement precision
If comprehensive disease analysis is performed on large data sets, then measurement precision improves, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and organizing physiological data into structured formats before analysis, and by using a trained disease state classifier that has already learned from comprehensive data sets. This preliminary preparation enables faster real-time analysis without sacrificing measurement precision, as the heavy computational lifting occurs during the offline training phase rather than during live analysis.
3Measurement precision
If detailed physiological data is collected and analyzed, then measurement precision improves, but loss of information decreases while device complexity increases
Solution Approach 1:
The disease state classifier acts as an intermediary between raw physiological data and curative pattern recommendations. It receives detailed physiological data, processes it through learned patterns, and outputs identified disease states. This intermediary layer simplifies the overall system complexity by providing a clear interface between data collection and treatment recommendation, while preserving measurement precision through systematic data processing.
Data Source
AI summary
A system for constitutional analysis using objective functions includes a computing device configured to generate a ranked list of diseases, by determining a plurality of disease impact score vectors for plurality of diseases, and generating and optimizing a first objective function of the impact score vectors, to receive, from a user, a plurality of user physiological history data, to identify, as a function of a disease state classifier, a plurality of disease states associated with the plurality of user physiological history data, to match at least a disease state of the plurality of disease states to the ranked list of diseases, and to generate a curative habitual pattern to alleviate the at least a disease state by combining intervention elements to form a curative habitual pattern candidates, calculating a curative impact score of each curative habitual pattern candidate, and selecting the curative habitual pattern using the curative impact score.