Microbiome-Based Delivery Date Estimation System
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Solution Overview
Problem
Current methods for predicting the expected delivery date (EDD) of a pregnant subject are inaccurate, especially in later stages of pregnancy, and fail to consider the pregnant mother's microbiome profile, leading to unnecessary medical interventions and logistical challenges.
Innovation Solution
A method and system using microbiome data to predict EDD by collecting biological samples, performing microbiome profiling, and inputting the data into a prebuilt ensemble model to calculate a central tendency value for EDD, incorporating machine learning techniques and k-fold cross validation to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ultrasound imaging measurements are used to estimate delivery date in later stages of pregnancy, then the procedure can be performed, but the accuracy decreases due to increased biological variations and approximation errors
Solution Approach 1:
The patent transitions from using anatomical measurements (ultrasound dimensions of fetus) to using biochemical parameters (microbiome composition and metabolic profiles) for delivery date prediction. This parameter change enables accurate predictions in later pregnancy stages by measuring molecular biomarkers that reflect physiological changes associated with impending delivery, overcoming the limitations of anatomical measurement variability.
2Reliability
If conventional ultrasound methods are used for delivery date prediction, then existing infrastructure can be utilized, but unnecessary medical interventions occur due to imprecise estimates
Solution Approach 1:
The patent replaces the mechanical measurement approach (ultrasound physical measurements of fetal anatomy) with a biochemical analysis system (microbiome sequencing and metabolic profiling). This substitution provides more reliable delivery date predictions by detecting molecular signatures of labor preparation, thereby reducing false predictions that lead to unnecessary medical interventions.
3Measurement precision
If microbiome profiling is performed to improve delivery date prediction accuracy, then prediction reliability increases, but the complexity of the system increases
Solution Approach 1:
The patent employs a multi-functional analytical system that simultaneously processes microbiome composition data, metabolic profiles, and temporal patterns from multiple time points. This unified system integrates diverse data types through machine learning algorithms to produce accurate delivery date predictions, managing complexity through a comprehensive yet coordinated analytical framework that leverages multiple information sources synergistically.
Data Source
AI summary
The need for an accurate, early, and precise estimation of expected delivery date (EDD) for the pregnant subject is vital. A system and method for predicting a day/date of delivery for a pregnant subject using one or more microbiome samples collected from the pregnant subject is provided. The disclosure relates to applying machine learning techniques on the microbiome characterization data corresponding to the biological sample(s) collected from the pregnant subject. The method further comprises using the predicted EDD to suitably plan and take required medical treatment or precautions or medical advice for the pregnant subject to prevent any pregnancy and/or delivery related complications and to manage the delivery appropriately. The disclosure also provides compositions of the microbiome data which can potentially influence the delivery date, or the method provides exemplary compositions of the microbiome data which plays vital role in estimating the EDD of the pregnant subject.