Uterine Activity Forecasting via Predictive Modeling
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Solution Overview
Problem
Current uterine activity monitoring during labor does not provide indicators of future contractions, limiting the ability to predict labor progression and respond appropriately.
Innovation Solution
A decision support system that uses predictive models, such as an ensemble of neural network ARIMA models, to forecast uterine activity values based on historical measurements, allowing for the prediction of future contractions and labor progression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional real-time monitoring is used, then current contraction detection is provided, but future contraction prediction is not available
Solution Approach 1:
The system performs preliminary actions by training predictive models on historical contraction data before actual labor occurs. The models are pre-configured to analyze patterns in uterine activity and generate forecasts of future contractions, enabling prediction capability to be ready before needed.
Solution Approach 2:
The system implements feedback loops where real-time contraction measurements are continuously fed back into the predictive models to refine forecasts. The models learn from actual contraction patterns and adjust their predictions, creating an adaptive system that improves accuracy over time based on observed labor progression.
2Measurement precision
If predictive models are trained on reference population data, then model accuracy is improved, but applicability to individual patients may be reduced
Solution Approach 1:
The system segments the prediction approach into two components: a general predictive model trained on reference population data providing baseline accuracy, and patient-specific customization using individual contraction patterns. This segmentation allows the system to leverage both population-level insights and patient-specific characteristics.
Solution Approach 2:
The system applies local quality by tailoring prediction parameters to each individual patient's contraction patterns while using the same foundational model architecture. Each patient's unique contraction characteristics are incorporated into the prediction, allowing the system to adapt to local variations in labor progression while maintaining overall model accuracy.
Data Source
AI summary
A decision support tool is provided for predicting uterine contractions. The predicted contractions are determined from measurements of uterine activity (UA). The contraction forecast may be made using a plurality of trained predictive models. The forecasts are formed with linear regressive models based, at least in part, on UA data from a reference population. The predicted contractions may be predictions of contraction duration, contraction peak, and time between contractions. In this way, these predicted contractions may be used for decision support for care plans during labor, such as increased monitoring and/or modifying the care plan.