Pregnancy Probability Prediction Algorithm Using Phenotypic Segmentation
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Solution Overview
Problem
Current methods for predicting the probability of achieving a pregnancy through fertility treatments, such as IVF, are inaccurate as they do not account for individual phenotypic traits and do not differentiate between first-time patients and those who have previously attempted treatment, leading to overly optimistic predictions.
Innovation Solution
A system that uses data from a cohort of women to analyze fertility-associated phenotypic traits and pregnancy outcomes over time, adjusting probabilities based on specific traits and treatment history to provide a more accurate prediction of pregnancy success.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cross-sectional reporting of pregnancy probability is used based on maternal age, then the prediction process is simple, but the prediction accuracy deteriorates because it does not account for treatment history and individual phenotypic traits
Solution Approach 1:
The prediction system segments the patient population into distinct groups based on treatment history (first-time patients vs. previous attempters) and further segments within each group based on phenotypic traits. This segmentation allows for customized prediction models that account for the different characteristics and outcomes of each subgroup, thereby improving prediction accuracy while maintaining operational feasibility through automated classification.
Solution Approach 2:
The system applies local quality by providing customized prediction models tailored to specific patient subgroups rather than using a single uniform model. Each subgroup (defined by treatment history and phenotypic traits) receives a prediction model optimized for their particular characteristics, ensuring that the prediction accuracy is locally optimized for each segment while the overall system remains manageable.
2Ease of operation
If standard IVF success rate statistics are reported to couples, then the information is easily communicated, but the information becomes misleading because it overestimates individual pregnancy probability
Solution Approach 1:
The system replaces generic success rate statistics with customized predictions tailored to each patient's specific phenotypic traits and treatment history. This localized approach ensures that the information communicated to each couple accurately reflects their individual probability of pregnancy success, eliminating the overestimation problem inherent in standard population-level statistics while maintaining clear communication.
Solution Approach 2:
The system incorporates feedback from treatment history data to adjust predictions for individual patients. By analyzing outcomes from previous treatment attempts and phenotypic characteristics, the system provides feedback-based predictions that are more realistic and personalized, preventing the misleading overestimation that occurs when standard statistics are applied universally.
3Measurement precision
If multiple phenotypic traits and treatment history data are analyzed, then prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The system employs a multi-functional prediction platform that can handle multiple phenotypic traits, treatment history data, and different patient subgroups through a single integrated system. This universal approach consolidates what would otherwise require multiple separate analysis tools, improving prediction accuracy through comprehensive data analysis while preventing system complexity from becoming unmanageable through centralized architecture.
Solution Approach 2:
The system manages complexity by dynamically adjusting which phenotypic traits and parameters are incorporated into predictions based on data availability and patient characteristics. Rather than always analyzing all possible parameters, the system adapts the analysis to the specific case, improving prediction accuracy when relevant data is available while reducing computational complexity when data is limited.
Data Source
AI summary
The present invention generally relates to systems and methods for determining the probability of a pregnancy at a selected point in time. Systems and methods of the invention employ an algorithm that has been trained on a reference set of data from a plurality of women for whom at least one of fertility-associated phenotypic traits, fertility-associated medical interventions, or pregnancy outcomes are known, in which the algorithm accounts for any woman who ceases pregnancy attempts prior to reaching a live birth outcome.