Machine Learning Hematological Profile Analysis for Nutritional Program Generation
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Solution Overview
Problem
Current systems face challenges in accurately tracking and predicting age-related biological degradations in hematological disorders due to difficulties in sampling and identifying physiological parameters effectively.
Innovation Solution
A machine-learning based system that acquires hematological data, retrieves profiles, classifies disorders, determines nutritional levels, and generates consumption programs to address these disorders by identifying relationships between nutritional elements and hematological parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sampling methods are used to track hematological parameters, then the system is simpler to implement, but the measurement precision and ability to capture degradation trajectories are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual sampling and tracking methods with a machine learning-based computational system. The system uses algorithms to analyze hematological data, identify degradation patterns, and predict future states, substituting manual processes with automated intelligent systems that achieve higher precision without requiring complex physical infrastructure
Solution Approach 2:
The system creates virtual representations (profiles) of hematological states based on sampled data. These profiles serve as simplified models that capture essential degradation characteristics, allowing the system to track and predict hematological changes without requiring continuous complex measurements of all physiological parameters
2Reliability
If comprehensive physiological parameters are sampled to improve degradation prediction, then the prediction accuracy improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system extracts and focuses on the most critical hematological parameters that are most indicative of degradation, rather than attempting to measure all physiological parameters. The machine learning algorithm identifies and prioritizes key indicators (such as hemoglobin, hematocrit, platelet counts) that provide the most reliable degradation signals, reducing measurement complexity while maintaining prediction reliability
Solution Approach 2:
The system performs preliminary analysis and classification of hematological data to identify degradation patterns before making predictions. By pre-processing the data and creating structured profiles that highlight degradation indicators, the system reduces the complexity of subsequent detection and measurement tasks while improving prediction reliability
Data Source
AI summary
A system for generating a program includes a computing device configured to acquire at least a hematological datum, retrieve a hematological profile as a function of the at least a hematological datum, classify the hematological profile to a hematological disorder bundle, determine, using the hematological disorder bundle and the hematological profile, at least a nutritional level, wherein determining includes identifying a hematological relationship relating an effect of a plurality of nutritional levels on the hematological disorder bundle and determining the at least a nutritional level as a function of the hematological relationship and the hematological profile, identify, using the at least a nutritional level, at least a nutrition element, and generate a consumption program as a function of the at least a nutrition element.