Computational Model for Lactation Efficiency Prediction
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Solution Overview
Problem
Current methods fail to accurately and timely diagnose lactation abnormalities, such as delayed lactogenesis II and inadequate milk production, which can lead to short-term and long-term lactation issues in mothers, often only identified through late clinical signs or direct evaluation by lactation consultants.
Innovation Solution
A computational model that utilizes physicochemical parameters measured in breast milk samples to predict lactation efficiency by comparing these parameters with predetermined values or time points, allowing for early detection of inadequate lactation and responsiveness to treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clinical evaluation methods are used to diagnose lactation abnormalities, then the diagnosis can be made by healthcare professionals, but the detection is delayed and only identifies extreme cases
Solution Approach 1:
The patent applies preliminary action by measuring physicochemical parameters in breast milk samples at early time points (day 1-3 postpartum) to predict lactation abnormalities before they manifest as clinical signs. The computational model analyzes parameters such as conductivity, sodium, potassium, and other compositional elements during the critical early lactation period to identify at-risk mothers prior to development of overt clinical problems.
Solution Approach 2:
The patent uses an intermediary approach by introducing a computational model that acts as a mediator between raw breast milk sample data and clinical diagnosis. The model processes physicochemical measurements and compares them against predetermined values to generate predictions about lactation status, serving as an intermediate analytical layer that bridges laboratory measurements and clinical interpretation.
2Reliability
If direct evaluation by lactation consultants is performed, then comprehensive assessment can be conducted, but it is resource-intensive and not routinely performed
Solution Approach 1:
The patent applies self-service by enabling the breast milk sample to provide diagnostic information autonomously through its inherent physicochemical properties. The computational model automatically analyzes the sample parameters without requiring complex manual evaluation procedures, allowing the biological material itself to carry diagnostic weight through its measurable characteristics.
Solution Approach 2:
The patent replaces the mechanical system of direct human evaluation by lactation consultants with an automated computational model that processes digital data. The system substitutes manual clinical assessment with algorithm-based analysis of physicochemical measurements, transforming a human-intensive process into an automated information-processing system.
3Measurement precision
If multiple physicochemical parameters are measured in breast milk samples, then prediction accuracy improves, but measurement complexity and cost increase
Solution Approach 1:
The patent applies segmentation by dividing the comprehensive lactation assessment into distinct measurable parameters (conductivity, sodium, potassium, glucose, protein, fat, calcium, phosphorus). Each parameter can be measured independently using standard laboratory techniques, allowing the system to build predictive accuracy through systematic accumulation of discrete, manageable measurements rather than requiring a single complex assay.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model achieves a sensitivity of 73%, specificity of 80%, and positive predictive value of 92% in identifying lactation abnormalities, providing real-time lactation efficiency assessment and enabling early intervention to prevent or mitigate lactation difficulties.
Implementation Method 1
a detector for determining an expression of at least one physicochemical parameter of the breast milk sample
Data Source
AI summary
The invention generally concerns methods and devices for determining inadequate lactation in female subject.