Health Status Score System Using ML for Missing Data
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Solution Overview
Problem
Personal health monitoring systems often provide suboptimal health score predictions due to incomplete data, as users may not have access to all necessary health-related parameters.
Innovation Solution
A system utilizing a machine learning data processing model to generate candidate records with complete parameter value combinations, including entered and estimated values, to provide a more accurate health status score, with the ability to scale results for user acceptance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If health monitoring systems use only entered parameter values from users, then the system is simple to operate, but the health score accuracy deteriorates due to incomplete data
Solution Approach 1:
The system performs self-service by automatically generating candidate parameter values and completing the dataset without requiring users to manually enter all parameters. The machine learning model fills in missing health parameters based on the limited user input, enabling the system to compensate for incomplete user-provided data while maintaining ease of operation.
Solution Approach 2:
The machine learning model acts as an intermediary between the limited user input and the comprehensive health assessment requirements. It generates candidate records that bridge the gap between sparse entered data and the complete parameter set needed for accurate health scoring, effectively mediating the data completeness issue.
2Measurement precision
If health monitoring systems require complete parameter data from users, then the health score accuracy improves, but the ease of operation deteriorates due to complex data collection requirements
Solution Approach 1:
The system applies partial action by only requiring users to enter a subset of parameters (at least three) rather than the complete set. The machine learning model then generates the remaining candidate parameter values, achieving sufficient data completeness for accurate health scoring without demanding excessive user input effort.
Solution Approach 2:
The system automates the data completion process through self-service mechanisms. The machine learning model automatically generates missing parameter values based on patterns learned from training data, eliminating the need for users to manually provide complete datasets while maintaining high measurement precision.
3Measurement precision
If the system generates multiple candidate records with estimated parameter values, then the health score accuracy improves through data completion, but the device complexity increases due to machine learning model execution
Solution Approach 1:
The machine learning model is pre-trained on comprehensive health data before deployment. This preliminary training action allows the model to store learned relationships and patterns, enabling it to efficiently generate accurate candidate parameter values during operation without requiring complex real-time computations, thus reducing operational device complexity.
Solution Approach 2:
The system creates simplified copies of complete health records by generating candidate records that mimic the structure and content of full datasets. These candidate records are synthetic copies generated from limited input, providing the appearance of complete data without requiring the actual collection of all original parameters, thereby managing system complexity.
Data Source
AI summary
A system is described that facilitates determining, based on input associated with a person, a health status score associated with the person. The input relates to parameter values of at least one parameter relating to traits of the person. The system receives the input, and a processor executes a first machine learning data processing model for generating, based on the input data, a plurality of candidate records. For each candidate record, a parameter value combination formed by entered parameter values and candidate parameter values forms a unique combination. The data processing model generates, for each candidate record, a likelihood value indicative of a probability that the parameter value combination of the candidate record provides a true representation of the traits of the person.