Meta-learning Health Scoring via Multi-Model Text Analysis
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Solution Overview
Problem
Existing automated health scoring techniques are limited by the availability and accuracy of data, often relying on isolated data points that provide a limited view of an entity's health, leading to inaccurate or misleading determinations.
Innovation Solution
The method employs meta-learning by retrieving and processing text data related to an entity using machine learning models to identify and match relevant data sources, incorporating fuzzy matching and vector representations to determine a holistic health score, which is then used for automated decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing automated health scoring techniques are used, then the process is simple and fast, but the accuracy and completeness of health scoring are limited due to reliance on isolated data points
Solution Approach 1:
The patent combines multiple machine learning models (first machine learning model for address detection, second machine learning model for health score calculation) to process text data comprehensively. This merging of models allows the system to leverage diverse data sources and processing approaches, thereby improving measurement precision while managing complexity through structured integration.
Solution Approach 2:
The patent introduces an intermediary process that retrieves text data from multiple data sources, extracts relevant information, and prepares it for model processing. This intermediary layer acts as a mediator between raw data and the machine learning models, enabling more accurate health scoring by filtering and preparing data systematically before analysis.
2Measurement precision
If more data sources are accessed to improve health scoring accuracy, then measurement precision improves, but loss of time increases due to additional data retrieval and processing
Solution Approach 1:
The patent performs preliminary actions by retrieving and processing text data from multiple data sources before the main health score calculation. The first machine learning model pre-processes the text data to identify addresses and relevant entities, which then guides the second machine learning model's analysis. This preliminary processing reduces the time needed for subsequent analysis by pre-organizing and filtering the data.
Data Source
AI summary
Aspects of the present disclosure provide techniques for automated health scoring through meta-learning. Embodiments include retrieving text data related to an entity that was provided by a user and providing one or more first inputs to a first machine learning model based on a subset of the text data. Embodiments include determining, based on an output from the first machine learning model, whether the text data includes an address. Embodiments include determining that the text data includes a name and determining, based on the address and the name, that one or more text results from one or more data sources relate to the entity. Embodiments include providing one or more second inputs to a second machine learning model based on the one or more text results and determining, based on an output from the second machine learning model, a health score for the entity.