Computer Network Architecture for Automated Patient Risk Scoring
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Solution Overview
Problem
Current computer network architectures for machine learning in the healthcare field struggle to accurately forecast patient treatment outcomes and improve predictive models efficiently, as they require manual recalibration and are not adaptable to new data sources without increasing labor costs.
Innovation Solution
A novel computer network architecture that automatically improves predictive models by recalibrating and reselecting algorithms based on expanding data sources, incorporating machine learning and artificial intelligence to forecast patient treatment outcomes by combining clinical and financial metrics, and using a pipeline of healthcare outcome models to select optimal predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual recalibration of predictive models is used, then model accuracy can be maintained, but labor costs increase and adaptability to new data sources decreases
Solution Approach 1:
The system automatically recalibrates and reselects predictive models using new data sources without human intervention. The architecture enables the model to self-upgrade by continuously learning from expanding data sources, eliminating the need for manual recalibration while maintaining and improving accuracy over time
Solution Approach 2:
The predictive model architecture is designed to be dynamic and adaptable, automatically adjusting to new data sources and conditions. The system transitions from static manual recalibration to dynamic automatic adaptation, allowing the model to evolve with changing data environments without increasing labor costs
2Reliability
If manual recalibration of predictive models is used, then model accuracy can be maintained, but labor costs increase
Solution Approach 1:
The system performs automatic recalibration without requiring human labor, using computational resources instead. The architecture enables self-upgrading of predictive models through automated processes that eliminate manual intervention, thereby reducing labor costs while maintaining model accuracy
Solution Approach 2:
The patent replaces the mechanical process of manual recalibration with an automated computational system. The architecture uses algorithmic processes and machine learning techniques to substitute human effort with automated model training and evaluation, reducing labor costs while improving efficiency
3Measurement precision
If complex predictive models are used to forecast patient outcomes, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the predictive modeling process into distinct automated stages: data preprocessing, model training, evaluation, and deployment. This modular architecture manages complexity by breaking down the complex predictive modeling task into manageable, automated components that can be executed systematically
Solution Approach 2:
The architecture creates a universal framework that handles multiple predictive modeling tasks through a single automated system. The multi-functional platform can process different data sources, train various model types, and evaluate predictions across multiple healthcare scenarios, reducing overall system complexity through consolidation
Data Source
AI summary
Embodiments in the present disclosure relate generally to computer network architectures for machine learning, and more specifically, to computer network architectures in the context of program rules, using combinations of defined patient clinical episode metrics and other clinical metrics, thus enabling superior performance of computer hardware. Aspects of embodiments herein are specific to patient clinical episode definitions, and are applied to the specific outcomes of highest concern to each episode type. Furthermore, aspects of embodiments herein produce more accurate and reliable predictions of possible patient outcomes and metrics.