Healthcare Data Analyzer for Reusable Predictive Model Deployment
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Solution Overview
Problem
Existing AI/ML solutions in healthcare are fragmented and piecemeal, focusing on single conditions for a small group of users without an underlying infrastructure to facilitate efficient deployment across health software platforms and data streams.
Innovation Solution
A computing system and method that integrates predictive and prescriptive models with healthcare data analyzers to receive, map, and input health data for generating predictions or actions, using a standardized, reusable pathway to deploy academically-trained AI/ML models effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive models are developed in research labs, then model accuracy and innovation are improved, but deployment complexity and implementation costs increase significantly
Solution Approach 1:
The patent introduces a healthcare data analyzer as an intermediary component that sits between research lab models and production healthcare systems. This analyzer receives predictive models from research, performs standardized data mapping and preprocessing, and outputs results in formats compatible with existing healthcare platforms, thereby resolving the deployment complexity without sacrificing model accuracy
Solution Approach 2:
The system segments the deployment process into distinct modular components: model reception module, data extraction module, mapping module, and output generation module. This segmentation allows each component to be independently developed, tested, and maintained, reducing overall deployment complexity while preserving the integrity and accuracy of the predictive models
2Measurement precision
If AI/ML models are customized for single conditions and small user groups, then model specialization and precision are improved, but system versatility and scalability deteriorate
Solution Approach 1:
The healthcare data analyzer is designed as a universal platform that can process multiple types of predictive models for different health conditions through a single standardized interface. It performs consistent data extraction, mapping, and preprocessing operations across diverse model types, enabling the system to serve multiple user groups and conditions without sacrificing condition-specific precision
3Productivity
If models are moved from lab to production, then practical impact and utility are improved, but financial cost and effort requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring the data analyzer with standardized mapping templates and preprocessing pipelines before actual model deployment. Common data transformation operations are prepared in advance, so when a predictive model needs to be deployed from research to production, the majority of the integration work has already been completed, significantly reducing implementation time and effort
Data Source
AI summary
Computing systems and methods for using a predictive model and health condition data to predict a healthcare outcome are disclosed. According to an aspect, a computing system include a healthcare data analyzer configured to receive a predictive model. The predictive model is adapted to use one or more inputs of a person's health data for predicting a healthcare outcome for the person. The healthcare data analyzer is configured to extract data of one or more risk factors from data indicative of the health condition of the person. The healthcare data analyzer is also configured to map the extracted data to the input(s) of the predictive model. Further, the healthcare data analyzer is configured to input the risk factor data into the predictive model to generate a prediction of the healthcare outcome for the person. The healthcare data analyzer presents the prediction of the healthcare outcome for the person.