Gestational Nourishment Program Generation via Maternal Marker Analysis
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Solution Overview
Problem
Current edible suggestion systems do not account for pregnancy, leading to inefficient nutrition plans for mothers and/or fetuses, and lack uniformity, resulting in poor developmental growth.
Innovation Solution
A system and method using a computing device to generate a gestational disorder nourishment program by obtaining a maternal marker, classifying conception data, determining gestational disorders using machine-learning models, calculating gestational phases, and generating nourishment programs based on these phases and outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current edible suggestion systems are used without pregnancy-specific adjustments, then general nutrition recommendations can be provided, but nutritional efficiency and fetal development outcomes deteriorate
Solution Approach 1:
The system transitions from uniform general nutrition recommendations to pregnancy-specific personalized plans by incorporating gestational phase, maternal markers, and fetal development stage. The nourishment program is locally optimized for each pregnancy condition rather than applying generic advice to all users.
Solution Approach 2:
The system dynamically adjusts nutrition recommendations based on changing gestational phases, maternal marker levels, and fetal development progress. The nourishment program evolves over time rather than remaining static, adapting to the dynamic nature of pregnancy.
2Ease of operation
If uniform nutritional plans are provided without considering gestational progression, then implementation simplicity is maintained, but fetal developmental growth deteriorates
Solution Approach 1:
The system changes key parameters including gestational phase classification, maternal marker thresholds, and nourishment recommendations based on fetal development stage. These parameter adjustments enable precise nutrition planning that matches fetal developmental needs at each gestational milestone.
Solution Approach 2:
The pregnancy period is segmented into distinct gestational phases with specific nutritional requirements. The system divides the continuous pregnancy process into manageable stages, each with tailored nourishment recommendations, making the complex information more actionable while maintaining precision.
3Measurement precision
If machine-learning models are trained with comprehensive disorder training sets, then gestational disorder detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary training of machine-learning models with comprehensive disorder training sets during system setup. This preliminary action prepares the models in advance to accurately detect gestational disorders during actual use, separating the complex training process from the simpler deployment phase.
Data Source
AI summary
A system for generating a gestational disorder nourishment program comprising a computing device, the computing device configured to obtain a maternal marker, calculate a gestational phase as a function of the maternal marker, wherein calculating the gestational phase further comprises, identifying a gestational goal, and calculating the gestational phase as a function of the maternal marker and the gestational goal as a function of a gestational machine-learning model, determine an edible as a function of the gestational phase, and generate a nourishment program as a function of the edible.