ADME Machine Learning for Personalized Supplement Selection
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Solution Overview
Problem
Consumers face challenges in accurately selecting supplements due to a lack of understanding about how these products are metabolized and distributed in their bodies, leading to potentially harmful consequences.
Innovation Solution
A system and method utilizing a computing device that receives a longevity inquiry, retrieves biological data, identifies relevant elements, and employs machine-learning algorithms to optimize supplement decisions based on ADME (Absorption, Distribution, Metabolism, Excretion) factors, selecting compatible supplements tailored to individual user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumers select supplements without understanding ADME factors, then supplement selection is simple and quick, but accuracy and safety of selection deteriorates
Solution Approach 1:
The system enables self-service by automatically analyzing user biological data and making supplement recommendations without requiring consumer expertise in ADME factors. The computing device performs the complex analysis work that would otherwise require specialized knowledge, allowing consumers to benefit from accurate personalized recommendations without needing to understand the underlying scientific complexity.
Solution Approach 2:
The system introduces an intermediary computing device that mediates between the complex ADME analysis and the consumer. This intermediary handles the complexity of evaluating absorption, distribution, metabolism, and excretion factors while presenting simplified supplement recommendations to the user, effectively shielding the consumer from the complexity while maintaining accuracy.
2Reliability
If a multi-factorial approach is used to evaluate supplement metabolism and distribution, then selection accuracy improves, but the time and resources required increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing user biological data in a database before supplement selection is needed. When a consumer seeks supplement recommendations, the system can quickly retrieve relevant biological data and perform ADME analysis without requiring time-consuming data collection or complex calculations at the moment of decision-making.
Solution Approach 2:
The system replaces the mechanical process of manual supplement evaluation with automated computing algorithms. The computing device automatically processes biological data, evaluates ADME factors, and generates supplement recommendations, substituting the time-consuming manual multi-factorial analysis with efficient automated computational processes that maintain reliability while reducing time requirements.
Data Source
AI summary
A system for optimizing supplement decisions is disclosed. The system includes a computing device configured to receive a longevity inquiry from a remote device. The system retrieves a biological extraction pertaining to a user and identifies a longevity element associated with a user. The system selects an ADME model utilizing a biological extraction. The system generates a machine-learning algorithm utilizing the selected ADME model to input a longevity element associated with a user as an input and output an ADME factor. The system identifies a second longevity element compatible with the ADME factor as a function of the first longevity element. The system selects the second longevity element as a tolerant longevity element. A method for optimizing supplement decisions is also disclosed.