Cohort-Based Predictive Data Analysis for Sparse Medical Inputs
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Solution Overview
Problem
Existing predictive data analysis systems are ill-suited for high-dimensionality and highly sparse input spaces, such as medical domains, due to complex relationships between input features and the lack of sufficient ground-truth data, leading to ineffective and inefficient predictive data analysis.
Innovation Solution
The system employs iterative refinement of predictive cohorts and dynamic training of models using external integration to generate optimized predictive models that can handle high-dimensionality and sparse data, optimizing interactions between predictive entities and improving model accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing predictive data analysis systems are used in high-dimensionality and highly sparse input spaces, then the systems can process complex input features, but the predictive accuracy and reliability deteriorate due to insufficient ground-truth data
Solution Approach 1:
The patent segments the high-dimensional input space by creating cohorts based on shared characteristics or patterns. Instead of treating all data points uniformly, the system divides them into smaller, more manageable cohorts that can be analyzed separately, thereby improving predictive reliability in sparse regions by leveraging local patterns within each cohort
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing data into cohorts before the actual predictive analysis. This includes creating factor scores, determining cohort memberships, and preparing segmented data structures in advance, which enables more reliable predictions when ground-truth data is limited
2Adaptability or versatility
If traditional predictive models are applied to high-dimensionality input spaces, then the models can accommodate multiple features, but the computational efficiency and processing speed deteriorate
Solution Approach 1:
By segmenting the data into cohorts based on shared characteristics, the system reduces the effective dimensionality of each sub-problem. This segmentation allows for more efficient computation within each cohort while maintaining the ability to handle high-dimensional input spaces overall
Solution Approach 2:
The system applies local quality by treating different cohorts with potentially different analytical approaches optimized for their specific characteristics. Each cohort can be processed with methods tailored to its local data properties, improving overall computational efficiency compared to applying a uniform approach to all high-dimensional data
3Loss of time
If predictive models are trained with limited ground-truth data in sparse domains, then the training process is faster, but the model reliability and generalization capability deteriorate
Solution Approach 1:
The patent segments the limited ground-truth data into cohorts, allowing the model to learn from localized patterns within each cohort even when overall data is sparse. This segmentation enables effective use of limited training data by concentrating learning signals within homogeneous groups, improving model reliability without requiring extensive training time
Solution Approach 2:
The system incorporates feedback mechanisms where prediction results are used to refine cohort definitions and factor scores iteratively. This feedback loop allows the model to improve its reliability progressively by learning from its own predictions and adjusting its cohort assignments, making effective use of limited ground-truth data through iterative refinement
Data Source
AI summary
There is a need for more effective and efficient predictive data analysis solutions. This need can be addressed by, for example, obtaining prediction input objects each associated with a predictive entity; performing iterations of an iterative cohort generation routine until a qualified predictive model is identified, wherein each iteration comprises determining one or more predictive cohorts for predictive entities based on the prediction input objects, generating a predictive model based on the predictive cohorts, performing a predictive inference based on the predictive model to generate a current iteration prediction, generating a predictive score based on the current iteration prediction, and determining whether the predictive model is the qualified predictive model based on whether the predictive score exceeds a predictive score threshold; and performing cohort-based predictive data analysis based on the qualified predictive model to generate a respective final prediction for each predictive entity.