Biological Extraction Recommendations Using Adherence Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Identifying a maximally effective set of lifestyle modifications is complex and uncertain due to varying effects based on underlying circumstances, making it challenging to determine ideal solutions across different situations.
Innovation Solution
A system and method utilizing machine-learning processes to generate lifestyle intervention recommendations based on biological extractions, including user physiological data, to derive user inclination and adherence, and select optimal interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learning models are used to generate lifestyle intervention recommendations, then personalization and effectiveness are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the recommendation generation process into distinct modules: biological extraction processing, machine-learning model inference, user inclination analysis, and intervention selection. This segmentation allows each component to be optimized independently while working together to produce personalized recommendations based on biological data.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates raw biological extraction data into standardized features that can be processed by machine-learning models. This intermediary layer includes normalization, feature extraction, and transformation steps that bridge the gap between raw biological data and actionable recommendations.
2Measurement precision
If multiple lifestyle intervention combinations are generated and evaluated, then recommendation accuracy is improved, but computational time and processing resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing biological extraction data, extracting relevant features, and preparing input datasets before they are fed into the machine-learning models. This preliminary processing reduces the computational burden during the actual recommendation generation phase and enables more accurate multi-combination evaluation within acceptable time frames.
Solution Approach 2:
The patent applies partial action by evaluating a subset of lifestyle intervention combinations rather than exhaustively testing all possible combinations. The machine-learning models prioritize and rank interventions based on predicted effectiveness and user inclination, allowing the system to provide accurate recommendations by focusing computational resources on the most promising interventions rather than all possibilities.
Data Source
AI summary
In an aspect, a system for generating lifestyle change recommendations based on biological extractions includes a computing device designed and configured for receiving a biological extraction pertaining to a user generating, using a first machine-learning process, a plurality of lifestyle intervention combinations as a function of the biological extraction, assigning, to each lifestyle intervention combination of the plurality of lifestyle intervention combinations, a degree of projected user adherence to the lifestyle intervention combination, wherein assigning further comprises performing a second machine learning process, and selecting, from the plurality of lifestyle intervention combinations, a lifestyle intervention combination as a function of the degree of projected user adherence of the selected lifestyle intervention combination.