Prediction Model for Socioeconomic Data Imputation
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Solution Overview
Problem
Existing data analytics for healthcare needs improvement in accurately forecasting population segment healthcare requirements due to erroneous and incomplete socioeconomic data, and suboptimal training processes for prediction models.
Innovation Solution
A system and method for computer-assisted preparation and use of prediction models that receive population segment data, extract training sets of parameter values, provide input to prediction models to predict additional values without reliance on existing values, and use reference feedback to train the models, facilitating accurate socioeconomic status predictions and healthcare resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If socioeconomic data is used to forecast healthcare needs, then healthcare resource allocation can be optimized, but the data may be erroneous and incomplete
Solution Approach 1:
The patent introduces prediction models as intermediary components that process socioeconomic data. These models act as mediators between the raw data and healthcare forecasting, transforming potentially erroneous data into refined predictions through multiple processing stages including data cleaning, feature extraction, and model training on historical healthcare outcomes.
Solution Approach 2:
The system implements feedback mechanisms where predicted healthcare needs are compared against actual outcomes to continuously refine the prediction models. This feedback loop allows the models to learn from past performance and improve their accuracy over time, compensating for initial data errors.
2Productivity
If prediction models are trained with existing data, then healthcare forecasts can be made, but the training process remains suboptimal
Solution Approach 1:
The patent performs preliminary data preparation actions before model training, including data cleaning, validation, and feature engineering. Historical data is pre-processed to remove errors and inconsistencies, and relevant features are extracted and transformed to optimize the subsequent training process, ensuring high-quality models from the outset.
Solution Approach 2:
The training process incorporates feedback mechanisms where model predictions are continuously evaluated against actual healthcare outcomes. This feedback is used to adjust training parameters, reweight data samples, and refine model architecture, creating an iterative optimization process that improves training quality over time.
3Adaptability or versatility
If socioeconomic data is collected from public databases, then population segment analysis is enabled, but the data is erroneous and incomplete
Solution Approach 1:
The prediction models serve as intermediaries that process and refine socioeconomic data from public databases. They perform data imputation to fill missing values, validate data consistency across multiple sources, and transform incomplete data into reliable predictions, thereby maintaining the ability to analyze diverse population segments while compensating for data deficiencies.
Data Source
AI summary
The present disclosure pertains to a system configured to prepare and use prediction models for socioeconomic data and missing value prediction. Some embodiments may: extract, from received population segment data, a training set of socioeconomic parameter values for each population segment; provide, to a prediction model as input, first parameter values of the respective training set for the prediction of additional parameter values of the training set such that the prediction of the additional parameter values is performed without reliance on the additional parameter values; provide, for each of the training sets, the additional parameter values to the prediction model as reference feedback for the prediction model's prediction of the additional parameter values to train the prediction model; and predict, based on a working set of parameter values for a population segment, additional values for the working set using the prediction model subsequent to its training.